BERD Resource by Keyword

Updated at 8:05 am on 23SEP2022

keyword All keywords for Resource Type of Resource Title URL Description Statistical knowledge level required (1=none, 4=masters level 6=PhD level) Relevant Statistical Packages Is there a cost associated with this resource Programming knowledge required (1=none to 6 professional programmer)
cox Model survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
intra-Class Correlation Coefficient multilevel analysis; hierarchical linear model; random coefficients models; intra-class correlation coefficient; Book Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling https://www.amazon.com/Multilevel-Analysis-Introduction-Advanced-Modeling/dp/184920201X/ref=mt_paperback?_encoding=UTF8&me= Introduction to multilevel analysis, topics include hierarchical linear model, random coefficients models, how to calculate ICC, how much does a model explain, etc. 5 SAS, SPSS, R, M+, Yes 4
sas Macros SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
    Journal Article Aspirin in the prevention of coronary disease. https://www.ncbi.nlm.nih.gov/pubmed/?term=2747748 Physicians' health study: aspirin and primary prevention of coronary heart disease. .   No .
$500 REDCap;variable length;trim;$500 Website Trimming the length of REDCap variables https://www.pharmasug.org/proceedings/2012/CC/PharmaSUG-2012-CC17.pdf The default length of non-validated text variables in REDCap is $500. This tends to bloat the resulting SAS data set. There is a SAS macro in the referenced web page that sets the length of the text variables to the maximum value actually found in the data. 1 SAS No 2
Active active;controls;bioequivalence Journal Article Evaluation of active control trials in AIDS. https://www.ncbi.nlm.nih.gov/pubmed/?term=2231306 Active Controls/Bioequivalence .   No .
Active active;controls;bioequivalence Journal Article Treatment evaluation in active control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=3119201 Active Controls/Bioequivalence .   No .
Adaptive Sampling Clinical Trials;Randomization;Control Groups ;Adaptive Sampling Journal Article The randomization and stratification of patients to clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=4612056 Clinical Trials: Randomization/Control Groups & Adaptive Sampling .   No .
Adaptive Sampling adaptive sampling Journal Article Sequential decision for a binomial parameter with delayed observations. https://www.ncbi.nlm.nih.gov/pubmed/?term=Sequential+decision+for+a+binomial+parameter+with+delayed+observations. adaptive sampling .   No .
Algorithm randomized clinical trials; sample size; algorithm;design; Journal Article A comprehensive algorithm for determining whether a run-in strategy will be a cost-effective design modification in a randomized clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8446807 In randomized clinical trials, poor compliance and treatment intolerance lead to reduced between-group differences, increased sample size requirements, and increased cost. A run-in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run-in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step-by-step algorithm through which one can estimate the cost-effectiveness of a potential run-in. Because the cost-effectiveness of a run-in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run-in on the required number of randomized, eligible, and screened subjects. Run-in strategies are most likely to be cost-effective when: (1) per patient costs during the post-randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run-in is inexpensive; (5) for most patients, the run-in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run-in are treatment intolerant or non-compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost-effectiveness of run-in strategies are stringent. In particular, if the only purpose of a run-in is to exclude ordinary partial compliers, the run-in will frequently add to the cost of the trial. Often, the cost-effectiveness of a run-in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects. .   No .
Alternatives ordered;alternatives;data;analysis Journal Article Analyzing data from ordered categories. https://www.ncbi.nlm.nih.gov/pubmed/6749191 Ordered Alternatives .   No .
Alternatives ordered;alternatives;data;analysis Journal Article Standards for the use of ordinal scales in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3080061 Ordered Alternatives .   No .
Alternatives ordered;alternatives;data;analysis Journal Article Ordinal scale and statistics in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3081161 Ordered Alternatives .   No .
Analysis Statistics; analysis; summaries Online Interactive Course Exploratory Data Analysis https://www.coursera.org/learn/exploratory-data-analysis Per the course website, this is course 4 of 10 in the Data Science Specialization. It is prepared by JHU and helps cover the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. . Any Yes .
Analysis methods;randomized trials;analysis; Journal Article A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8500308 The standard approach to analyzing randomized trials ignores information on postrandomization compliance. Application of these methods results in estimates that may lack the desired causal interpretation. We employ a new method of estimation and analyze data from the Multiple Risk Factor Intervention Trial (MRFIT) to estimate the causal effect of quitting cigarette smoking. Our procedure utilizes a method proposed by Robins and Tsiatis and allows us to take advantage of postrandomization smoking history without requiring untenable assumptions about the comparability of compliers and noncompliers. We contrast the performance of our method and the standard intent-to-treat analysis in the MRFIT data and in simulated data in which compliance rates are varied. .   No .
Analysis methods;analysis;clinical trial;randomization; Journal Article Adjusting for non-compliance and contamination in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9160496 A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive. .   No .
Analysis analysis;regression models;methods Journal Article An annotated bibliography of methods for analysing correlated categorical data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1557577 This paper provides an annotated bibliography of over 100 articles concerning methods for analysing correlated categorical response data. Most of the papers listed here concern categorical regression models and estimation, with particular emphasis on binary responses. The papers are classified by several characteristics which group them according to common themes. The bibliography serves as a reference of methods for analysts of correlated categorical data, as well as for persons interested in methodologic work in this active area of statistical research. .   No .
Analysis analysis;randomized trials; Journal Article Analysis of data from group-randomized trials with repeat observations on the same groups. https://www.ncbi.nlm.nih.gov/pubmed/?term=9699231 This study used Monte Carlo simulations to evaluate the performance of alternative models for the analysis of group-randomized trials having more than two time intervals for data collection. The major distinction among the models tested was the sampling variance of the intervention effect. In the mixed-model ANOVA, the sampling variance of the intervention effect is based on the variance among group x time-interval means. In the random coefficients model, the sampling variance of the intervention effect is based on the variance among the group-specific slopes. These models are equivalent when the design includes only two time intervals, but not when there are more than two time intervals. The results indicate that the mixed-model ANOVA yields unbiased estimates of sampling variation and nominal type I error rates when the group-specific time trends are homogenous. However, when the group-specific time trends are heterogeneous, the mixed-model ANOVA yields downwardly biased estimates of sampling variance and inflated type I error rates. In contrast, the random coefficients model yields unbiased estimates of sampling variance and the nominal type I error rate regardless of the pattern among the groups. We discuss implications for the analysis of group-randomized trials with more than two time intervals. .   No .
Analysis clinical trials;bayesian;analysis;p value Journal Article Clinical trials and statistical verdicts: probable grounds for appeal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6830080 Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions. .   No .
Analysis group-randomized trials; design; analysis Journal Article Design and analysis of group-randomized trials: a review of recent methodological developments. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998806 We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials. .   No .
Analysis design;analysis;group-randomized trials; methods Journal Article Design and analysis of group-randomized trials: a review of recent practices. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998802 We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published. .   No .
Analysis design; analysis;randomized clinical trials Journal Article Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. analysis and examples. https://www.ncbi.nlm.nih.gov/pubmed/?term=831755 Part II of report which describes efficient methods of analysis of randomized clinical trials in which we wish to compare the duration of survival (or the time until some other untoward event first occurs) among different groups of patients. .   No .
Analysis analysis Journal Article Effects of physical training in chronic heart failure. https://www.ncbi.nlm.nih.gov/pubmed/?term=1967416 Eleven patients with chronic heart failure secondary to ischaemic heart disease (mean [SEM] age 63.0 [2.3] years; left ventricular ejection fraction 19 [8]% undertook 8 weeks of home-based bicycle exercise training and 8 weeks of activity restriction (rest) in a physician-blind, random-order, crossover trial. Training increased exercise duration from 14.2 (1.1) min to 16.8 (1.3) min and peak oxygen consumption from 14.3 (1.1) ml.min-1.kg-1 to 16.7 (1.3) ml.min-1.kg-1. Heart rates at submaximum workloads and rate-pressure products were significantly reduced by training, and there was also a significant improvement in patient-rated symptom scores. No adverse events occurred during the training phase. Thus home-based physical training programmes are feasible even in severe chronic heart failure and have a beneficial effect on exercise tolerance, peak oxygen consumption, and symptoms. The commonly held belief that rest is the mainstay of treatment of chronic heart failure should no longer be accepted. .   No .
Analysis randomized clinical trials;bias;methodology;analysis Journal Article Intention-to-treat analysis in randomized trials: who gets counted? https://www.ncbi.nlm.nih.gov/pubmed/?term=9378838 This article discusses the rationale and implications associated with the selection and use of analysis strategies for randomized clinical trials as they relate to protocol deviations. The topics addressed specifically are the conceptual and methodologic approaches and biases of clinical efficacy and effectiveness assessment. The authors suggest that different analytic strategies may be more or less appropriate depending on the intended audience. .   No .
Analysis analysis;clinical trials;randomization;intent-to-treat; Journal Article Intention-to-treat vs. on-treatment analyses of clinical trial data: experience from a study of pyrimethamine in the primary prophylaxis of toxoplasmosis in HIV-infected patients. ANRS 005/ACTG 154 Trial Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=9620807 Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. This paper presents several approaches used for analyzing data of a recent trial and the difficulties encountered in interpreting the results of each approach. Although exploratory analyses may yield clinically relevant information and useful clarifications in the evaluation of treatments, intention-to-treat remains the only interpretable analysis of clinical trials. .   No .
Analysis logistic regression;analysis Journal Article Long-term metered-dose inhaler adherence in a clinical trial. The Lung Health Study Research Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=7633711 Poor adherence to medication regimens is a well-documented phenomenon in clinical practice and an ever-present concern in clinical trials. Results of multiple logistic regression analysis indicate that the best compliance was found in participants who were married, older, white, had more severe airways obstruction, less shortness of breath, and fewer hospitalizations, and who had not been confined to bed for respiratory illnesses.(ABSTRACT TRUNCATED AT 250 WORDS). .   No .
Analysis analysis Journal Article Statistical aspects of the analysis of data from retrospective studies of disease. https://www.ncbi.nlm.nih.gov/pubmed/?term=13655060 Statistical aspects of the analysis of data from retrospective studies of disease. .   No .
Analysis clinical trials;analysis;repeated measures; confidence intervals Journal Article Statistical problems in the reporting of clinical trials. A survey of three medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=3614286 Reports of clinical trials often contain a wealth of data comparing treatments. This can lead to problems in interpretation, particularly when significance testing is used extensively. We examined 45 reports of comparative trials published in the British Medical Journal, the Lancet, or the New England Journal of Medicine to illustrate these statistical problems. The issues we considered included the analysis of multiple end points, the analysis of repeated measurements over time, subgroup analyses, trials of multiple treatments, and the overall number of significance tests in a trial report. Interpretation of large amounts of data is complicated by the common failure to specify in advance the intended size of a trial or statistical stopping rules for interim analyses. In addition, summaries or abstracts of trials tend to emphasize the more statistically significant end points. Overall, the reporting of clinical trials appears to be biased toward an exaggeration of treatment differences. Trials should have a clearer predefined policy for data analysis and reporting. In particular, a limited number of primary treatment comparisons should be specified in advance. The overuse of arbitrary significance levels (for example, P less than 0.05) is detrimental to good scientific reporting, and more emphasis should be given to the magnitude of treatment differences and to estimation methods such as confidence intervals. .   No .
Analysis repeated;measures;analysis Journal Article Analysis of serial measurements in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2106931 repeated measures analysis .   No .
Analysis repeated measures;analysis Journal Article Analysis of serial measurements in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2106931 Repeated measures analysis .   No .
Analysis repeated measures;analysis Journal Article Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design. https://www.ncbi.nlm.nih.gov/pubmed/1485053 Repeated measures analysis .   No .
Analysis repeated measures;analysis Journal Article Modelling covariance structure in the analysis of repeated measures data. https://www.ncbi.nlm.nih.gov/pubmed/10861779 Repeated measures analysis .   No .
Analysis power sample size;analysis;variance design; Journal Article Overcoming feelings of powerlessness in "aging" researchers: a primer on statistical power in analysis of variance designs. https://www.ncbi.nlm.nih.gov/pubmed/?term=9100270 Power and Sample size .   No .
Analysis multicenter trials;analysis;data Journal Article Guidelines for quality assurance in multicenter trials: a position paper. https://www.ncbi.nlm.nih.gov/pubmed/?term=9741868 Multicenter trials: Analysis of Data .   No .
Analysis multicenter trials;analysis;data Journal Article Publications from multicentre clinical trials: statistical techniques and accessibility to the reader. https://www.ncbi.nlm.nih.gov/pubmed/?term=7701142 Multicenter Trials: analysis of Data .   No .
Analysis multicenter trials;analysis;data Journal Article Evaluation of multicentre clinical trial data using adaptations of the Mosteller-Tukey procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341865 Multicenter Trials: Analysis of Data .   No .
Analysis multicenter trials;analysis;data Journal Article Tests for qualitative treatment-by-centre interaction using a 'pushback' procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341864 Multicenter trials: Analysis of Data .   No .
Analysis ordered;alternatives;data;analysis Journal Article Analyzing data from ordered categories. https://www.ncbi.nlm.nih.gov/pubmed/6749191 Ordered Alternatives .   No .
Analysis ordered;alternatives;data;analysis Journal Article Standards for the use of ordinal scales in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3080061 Ordered Alternatives .   No .
Analysis ordered;alternatives;data;analysis Journal Article Ordinal scale and statistics in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3081161 Ordered Alternatives .   No .
Analysis multiple;comparisons;data;analysis Journal Article Gatekeeping Strategies for Avoiding False-Positive Results in Clinical Trials With Many Comparisons. https://www.ncbi.nlm.nih.gov/pubmed/?term=29049572 Multiple Comparisons .   No .
Analysis multiple;comparisons;data;analysis Journal Article P-value interpretation and alpha allocation in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/9708870 Multiple Comparisons .   No .
Analysis multiple comparisons;data;analysis Journal Article Invited commentary: Re: "Multiple comparisons and related issues in the interpretation of epidemiologic data". https://www.ncbi.nlm.nih.gov/pubmed/?term=9583708 Multiple Comparisons .   No .
Analysis multiple;comparisons;data;analysis Journal Article Multiple comparisons and related issues in the interpretation of epidemiologic data https://www.ncbi.nlm.nih.gov/pubmed/?term=7572970 Multiple Comparisons .   No .
Analysis multiple;comparisons;data;analysis Journal Article Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8629727 Multiple Comparisons .   No .
Analysis multiple;comparisons;data;analysis Journal Article Impact of multiple comparisons in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3661589 Multiple Comparisons .   No .
Analysis multiple;comparisons;data;analysis Journal Article Multiple hypothesis tests in multiple investigations. https://www.ncbi.nlm.nih.gov/pubmed/?term=7792449 Multiple Comparisons .   No .
Analysis group;sequential;tests;design;analysis;data Journal Article Group sequential methods in the design and analysis of clinical trials https://www.jstor.org/stable/2335684?seq=1#metadata_info_tab_contents Group Sequential Tests .   No .
Analysis Of Covariance biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Analysis Tools Statistics; analysis tools Online Interactive Course Understanding Your Data: Analytical Tools https://www.coursera.org/learn/uva-darden-understanding-data-tools Per the course website, this course is prepared by the University of Virginia. By the end of this course, you will understand the differences between mediation and moderation and between moderated mediation and mediated moderation models (conditional indirect effects), and the importance of multilevel analysis. Most important, you will be able to run mediation, moderation, conditional indirect effect and multilevel models and interpret the results. . SPSS No .
Anova ANOVA Journal Article Testing pairwise contrasts in one-way analysis of variance designs https://www.ncbi.nlm.nih.gov/pubmed/?term=3786634 ANOVA .   No .
Anova ANOVA Journal Article Uses and abuses of analysis of co-variance in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3987300 ANOVA .   No .
Anova power sample size;regression;anova Journal Article Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system. https://www.ncbi.nlm.nih.gov/pubmed/?term=2507829 Power/sample size .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article Geographic variation in the treatment of localized breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=1552910 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article Hospital use and mortality among medicare beneficiaries in Boston and New Haven. https://www.ncbi.nlm.nih.gov/pubmed/?term=2677726 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article Predicting the appropriate use of carotid endarteretomy, upper gastrointestinal endoscopy, and coronary angiography. https://www.ncbi.nlm.nih.gov/pubmed/?term=2215595 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article Relation between surgeons' practice vlaumes and geographic variations in the rate of carotid endarterectomy. https://www.ncbi.nlm.nih.gov/pubmed/?term=2671727 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article The appropriateness of performing coronary artery bypass surgery. https://www.ncbi.nlm.nih.gov/pubmed/?term=2968469 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article The effectiveness of right heart catheterization in the initial care of critically ill patients. https://www.ncbi.nlm.nih.gov/pubmed/?term=8782638 Appropriateness of Medical Procedures .   No .
Appropriateness Of Medical Procedures Appropriateness of Medical Procedures Journal Article Variations in the use of medical and surgical services by the medicare population. https://www.ncbi.nlm.nih.gov/pubmed/?term=3510394 Appropriateness of Medical Procedures .   No .
Attributable Risk Attributable Risk Journal Article Attributable Risk: Advantages of a Broad Definition of Exposure https://www.ncbi.nlm.nih.gov/pubmed/?term=8059765 Attributable Risk .   No .
Attributable Risk Case Control Studies; attributable risk Journal Article Attributable Risk Estimation in Case-Control Studies https://www.ncbi.nlm.nih.gov/pubmed/?term=7925727 Case Control Studies, attributable risk .   No .
Attributable Risk Attributable Risk Journal Article Comparison of adjusted attributable risk estimators. https://www.ncbi.nlm.nih.gov/pubmed/?term=1293670 Attributable Risk .   No .
Attributable Risk Attributable Risk Journal Article Estimating the population atttributable risk for multiple risk factors using case-control data. https://www.ncbi.nlm.nih.gov/pubmed/?term=4050778 Attributable Risk .   No .
Attributable Risk Attributable Risk Journal Article Methods of adjustment for estimating the attributable risk in case-control studies: a review https://www.ncbi.nlm.nih.gov/pubmed/?term=11252017 Attributable Risk .   No .
Auc diagnostic medicine; ROC; AUC; sample size calculation for diagnostic test Book Statistical Methods in Diagnostic Medicine https://www.amazon.com/Statistical-Methods-Diagnostic-Medicine-Xiao-Hua/dp/0470183144/ref=sr_1_1?s=books&ie=UTF8&qid=1515100984&sr=1-1&keywords=statistical+methods+in+diagnostic+medicine basic concepts and methods in diagnostic medicine such as ROC and AUC, estimation and hypothesis testing, sample size calculation for sensitivity, specificity, ROC and AUC, regression analysis for independent and correlated ROC data 6 Any Yes 4
Authorship authorship; criteria Journal Article Criteria for authorship for statisticians in medical papers. https://www.ncbi.nlm.nih.gov/pubmed/9819828 Publication committees .   No .
Bayes Theorem clinical Trials comparative Effectiveness Research Bayes Theorem Clinical Trials Comparative Effectiveness Research Video Instruction Demonstration of Adaptive Study Design (12 min video) http://annals.org/aim/multimedia-player/12975799 Introduction to Bayesian Adaptive Methods for Comparative Effectiveness Research (video simulation). 3   No 2
Bayesian Bayesian Online Interactive Course Bayesian Statistics: From Concept to Data Analysis https://www.coursera.org/learn/bayesian-statistics Per the course website, this course is prepared by the University of California, Santa Cruz. This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses. . Any Yes .
Bayesian Bayesian Online Interactive Course Bayesian Statistics: Techniques and Models https://www.coursera.org/learn/mcmc-bayesian-statistics Per the course website, this course is prepared by the University of California, Santa Cruz, and it is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our . Any Yes .
Bayesian clinical trials;bayesian;analysis;p value Journal Article Clinical trials and statistical verdicts: probable grounds for appeal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6830080 Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions. .   No .
Bayesian Statistics Bayesian Statistics Online Interactive Course Bayesian Statistics https://www.coursera.org/learn/bayesian Per the course website, this course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes . Any Yes .
Bayesian Statistics Bayesian statistics; frequentist statistics Journal Article Statistical Inference: The Big Picture https://www.ncbi.nlm.nih.gov/pubmed/21841892 Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction. 2   No 1
Bias controlled clinical trials;bias;randomization;bias Journal Article Bias in treatment assignment in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=6633598 Controlled clinical trials of the treatment of acute myocardial infarction offer a unique opportunity for the study of the potential influence on outcome of bias in treatment assignment. A group of 145 papers was divided into those in which the randomization process was blinded (57 papers), those in which it may have been unblinded (45 papers), and those in which the controls were selected by a nonrandom process (43 papers). At least one prognostic variable was maldistributed (P less than 0.05) in 14.0 per cent of the blinded-randomization studies, in 26.7 per cent of the unblinded-randomization studies, and in 58.1 per cent of the nonrandomized studies. Differences in case-fatality rates between treatment and control groups (P less than 0.05) were found in 8.8 per cent of the blinded-randomization studies, 24.4 per cent of the unblinded-randomization studies, and 58.1 per cent of the nonrandomized studies. These data emphasize the importance of keeping those who recruit patients for clinical trials from suspecting which treatment will be assigned to the patient under consideration. .   No .
Bias controlled clinical trials;bias;randomization;bias Journal Article Bias in treatment assignment in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=6633598 Controlled clinical trials of the treatment of acute myocardial infarction offer a unique opportunity for the study of the potential influence on outcome of bias in treatment assignment. A group of 145 papers was divided into those in which the randomization process was blinded (57 papers), those in which it may have been unblinded (45 papers), and those in which the controls were selected by a nonrandom process (43 papers). At least one prognostic variable was maldistributed (P less than 0.05) in 14.0 per cent of the blinded-randomization studies, in 26.7 per cent of the unblinded-randomization studies, and in 58.1 per cent of the nonrandomized studies. Differences in case-fatality rates between treatment and control groups (P less than 0.05) were found in 8.8 per cent of the blinded-randomization studies, 24.4 per cent of the unblinded-randomization studies, and 58.1 per cent of the nonrandomized studies. These data emphasize the importance of keeping those who recruit patients for clinical trials from suspecting which treatment will be assigned to the patient under consideration. .   No .
Bias compliance;bias Journal Article Coronary heart disease and estrogen replacement therapy. Can compliance bias explain the results of observational studies? https://www.ncbi.nlm.nih.gov/pubmed/?term=8205277 The overall risk/benefit of estrogen replacement therapy (ERT) is strongly dependent on assumptions about the effect of ERT on coronary heart disease (CHD). The belief that ERT causes a substantial reduction in the risk of CHD is widespread. The studies that provide support for this belief are all nonexperimental ones. Three analyses of data from two randomized clinical trials of drug treatments for CHD have examined the association of compliance with total mortality in persons who complied with the taking of placebo. In these analyses, compliance with the taking of a placebo was associated with a reduction in overall mortality of the same magnitude as the reduction in the risk of CHD in users of ERT. The benefit of compliance with placebo was not reduced by adjustment for a large number of variables, both medical and sociodemographic, that might affect mortality. Users of ERT are compliers, and the possibility that compliance bias may account for some of the apparent benefit of ERT for CHD must be taken seriously. .   No .
Bias randomized clinical trials;bias;methodology;analysis Journal Article Intention-to-treat analysis in randomized trials: who gets counted? https://www.ncbi.nlm.nih.gov/pubmed/?term=9378838 This article discusses the rationale and implications associated with the selection and use of analysis strategies for randomized clinical trials as they relate to protocol deviations. The topics addressed specifically are the conceptual and methodologic approaches and biases of clinical efficacy and effectiveness assessment. The authors suggest that different analytic strategies may be more or less appropriate depending on the intended audience. .   No .
Bias methodology;bias;randomized trials;design Journal Article Methodological bias in cluster randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=15743523 Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials. Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants. .   No .
Bias methodology;bias;meta-analysis Journal Article Methodology and overt and hidden bias in reports of 196 double-blind trials of nonsteroidal antiinflammatory drugs in rheumatoid arthritis. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702836%5Buid%5D Important design aspects were decreasingly reported in NSAID trials over the years, whereas the quality of statistical analysis improved. In half of the trials, the effect variables in the methods and results sections were not the same, and the interpretation of the erythrocyte sedimentation rate in the reports seemed to depend on whether a significant difference was found. Statistically significant results appeared in 93 reports (47%). In 73 trials they favored only the new drug, and in 8 only the active control. All 39 trials with a significant difference in side effects favored the new drug. Choice of dose, multiple comparisons, wrong calculation, subgroup and within-groups analyses, wrong sampling units (in 63% of trials for effect variables, in 23% for side effects), change in measurement scale before analysis, baseline difference, and selective reporting of significant results were some of the verified or possible causes for the large proportion of results that favored the new drug. Doubtful or invalid statements were found in 76% of the conclusions or abstracts. Bias consistently favored the new drug in 81 trials, and the control in only one trial. It is not obvious how a reliable meta-analysis could be done in these trials. .   No .
Bias randomized controlled trial; bias;meta-analysis Journal Article Systematic review of the empirical evidence of study publication bias and outcome reporting bias. https://www.ncbi.nlm.nih.gov/pubmed/?term=18769481 The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention. Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials. .   No .
Bias interpretation; bias;study design Journal Article Why most published research findings are false. https://www.ncbi.nlm.nih.gov/pubmed/16060722 Interpretation, bias, study design .   No .
Bias (Epidemiology) power analysis; bias (epidemiology); model adequacy; type I error; cox proportional hazards models; logistic regression. Journal Article Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression https://academic.oup.com/aje/article/165/6/710/63906 Commentary and simulation about back-of-the-envelope power analyses for logistic and Cox regression. 2 Any No 1
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. https://www.ncbi.nlm.nih.gov/pubmed/16522836 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Lessons from and cautions about noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16522840 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Bioequivalence priority;equivalence;trials;bioequivalence Journal Article An approximate unconditional test of non-inferiority between two proportions. https://www.ncbi.nlm.nih.gov/pubmed/?term=10931513 Nonin Priority/ Equivalence Trials - Bioequivalence .   No .
Bioequivalence priority;equivalence;trials;bioequivalence Journal Article Equivalence Trials https://www.ncbi.nlm.nih.gov/pubmed/?term=9329939 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Significance testing to establish equivalence between treatments, with special reference to data in the form of 2X2 tables. https://www.ncbi.nlm.nih.gov/pubmed/?term=588654 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Bioequivalence revisited. https://www.ncbi.nlm.nih.gov/pubmed/?term=1485060 Nonin priority/Equivalence trials - Bioequivalence. .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Conventional null hypothesis testing in active control equivalence studies. https://www.ncbi.nlm.nih.gov/pubmed/8582153 Nonin priority/Equivalence Trials - Bioequivalence .   No .
Bioequivalence nonin;priority;equivalence;trials;bioequivalence Journal Article A comparison of continuous infusion of alteplase with double-bolus administration for acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/9340504 Nonin prioritiy/Equivalence Trials - bioequivalence .   No .
Bioequivalence active;controls;bioequivalence Journal Article Evaluation of active control trials in AIDS. https://www.ncbi.nlm.nih.gov/pubmed/?term=2231306 Active Controls/Bioequivalence .   No .
Bioequivalence active;controls;bioequivalence Journal Article Treatment evaluation in active control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=3119201 Active Controls/Bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Sample size determination in stratified trials to establish the equivalence of two treatments. https://www.ncbi.nlm.nih.gov/pubmed/?term=8677403 Power and Sample size Bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Sample size requirements for evaluating a conservative therapy. https://www.ncbi.nlm.nih.gov/pubmed/?term=688245 Power and sample size: Bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Comparison of tests and sample size formulae for proving therapeutic equivalence based on the difference of binomial probabilities. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481195 Power/sample size: bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article "Proving the null hypothesis" in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7160191 Power/sample size: bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Sample size determination for proving equivalence based on the ratio of two means for normally distributed data. https://www.ncbi.nlm.nih.gov/pubmed/?term=9990695 Power and sample size: bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Estimation and sample size considerations for clustered binary responses. https://www.ncbi.nlm.nih.gov/pubmed/?term=7973205 Power and sample size; bioequivalence .   No .
Bioequivalence power;sample size;bioequivalence Journal Article Sample size calculations for clustered binary data. https://www.ncbi.nlm.nih.gov/pubmed/?term=11427953 Power and sample size;bioequivalence .   No .
Bioinformatics certificate; BioInformatics; Informatics Online Interactive Course Public Health Informatics https://www.jhsph.edu/academics/certificate-programs/certificates-for-hopkins-and-non-degree-students/public-health-informatics.html Certificate in BioInformatics offered by JHU/ Bloomberg School of Public Health . Any, SAS Yes .
Biology biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Biostatistical biostatistical;cores Journal Article The biostatistician in medical research: allocating time and effort. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481203 Biostatistical Cores .   No .
Biostatistics Biostatistics Online Interactive Course Introduction to Biostatistics 2: Variables CE https://learning.umn.edu/search/publicCourseSearchDetails.do?method=load&courseId=1750057&selectedProgramAreaId=18870&selectedProgramStreamId=18877 Per the course website, the course will describe the purpose and fundamentals of different types of variables, including: quantitative and qualitative variables, continuous and discrete scales, nominal and ordinal data, and binary data It will also identify the following visual representations of data: stem and leaf plot, box plot, histogram, bar chart, and pie chart . Any Yes .
Biostatistics Biostatistics Online Interactive Course Introduction to Biostatistics CE https://learning.umn.edu/search/publicCourseSearchDetails.do?method=load&courseId=1749970 Per the course website, this course will help module learners to describe the purpose and fundamentals of sample statistics, breakdown the process of creating a histogram, define normal population distribution, and explain the 68%-95%-99-75% rule . Any Yes .
Biostatistics compliance;biostatistics Journal Article Clinical biostatistics. XXX. Biostatistical problems in 'compliance bias'. https://www.ncbi.nlm.nih.gov/pubmed/?term=4426153 Clinical biostatistics. XXX. Biostatistical problems in 'compliance bias'. .   No .
Biostatistics biostatistics;cores Journal Article Guidelines for budgeting biostatistics involvement in research projects. https://www.ncbi.nlm.nih.gov/pubmed/?term=8910957 Biostatistics Cores .   No .
Bjork-Shiley medical practice problems;strut fracture;Bjork-Shiley; Journal Article Risk of strut fracture of Björk-Shiley valves. https://www.ncbi.nlm.nih.gov/pubmed/1346279 Medical practice problems .   No .
Bootstrap resampling; bootstrap; jackknife; cross-validation; simulation Website Don't Be Loopy: Re-Sampling and Simulation the SAS Way http://www2.sas.com/proceedings/forum2007/183-2007.pdf An excellent paper by David Cassell presented the SAS user group about how to program resampling statistics in SAS. The most common way that people do simulations and re-sampling plans in SAS® is, in fact, the slow and awkward way. People tend to think in terms of a huge macro loop wrapped around a piece of SAS code, with additional chunks of code to get the outputs of interest and then to weld together the pieces from each iteration. But SAS is designed to work with by-processing, so there is a better way. A faster way. This paper will show a simpler way to perform bootstrapping, jackknifing, cross-validation, and simulations from established populations. It is simpler and more efficient to get SAS to build all the iterations in one long SAS data set, then use by-processing to do all the computations at once. This lets us use SAS features to gather automatically the information from all the iterations, for simpler computations afterward. 4 SAS No 3
Bootstrap Exact confidence intervals for a proportion; bootstrap; competing risk; case-control matching; survival analysis Software to Download Locally Written SAS Macros http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros The SAS macros below were written and are maintained by Mayo Clinic staff. They contain SAS source code, a brief description of the macro's function and an example of the macro call. 4 SAS No 3
Bootstrapping Bootstrapping Journal Article Bootstrapping: a tool for clinical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2286694 Bootstrapping .   No .
Calculator power; sample size; calculator; multivariate Interactive Program GLIMPPSE online Power and Sample Size Calculation http://glimmpse.samplesizeshop.org Well-documented online power calculator with guided steps. "GLIMMPSE can compute power or sample size for univariate and multivariate linear models with Gaussian errors." 59-page user manual is available at http://samplesizeshop.org/files/2012/08/GLIMMPSEUserManual_v2.0.0.pdf. 4   No 1
Cancer Cancer;Clinical Trials Design Journal Article Optimal two-stage designs for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702835 Cancer: Clinical Trials Design .   No .
Cardiology meta analysis;cardiology Journal Article Effect of intravenous nitrates on mortality in acute myocardial infarction: an overview of the randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/2896919 Meta Analysis Papers-Cardiology .   No .
Cardiology meta analysis;cardiology Journal Article Effects of prophylactic lidocaine in suspected acute myocardial infarction. An overview of results from the randomized, controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/3047448 Meta Analysis - Cardiology .   No .
Cardiology meta analysis;cardiology Journal Article The effect of low-dose warfarin on the risk of stroke in patients with nonrheumatic atrial fibrillation. https://www.ncbi.nlm.nih.gov/pubmed/2233931 Meta analysis - cardiology .   No .
Cardiology meta analysis; cardiology Journal Article The effect of warfarin on mortality and reinfarction after myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/2194126 Meta analysis - cardiology .   No .
Cardiology meta analysis; cardiology Journal Article Meta-analytic evidence against prophylactic use of lidocaine in acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=PMID%3A+2688587 Meta Analysis - Cardiology .   No .
Cardiology meta analysis; cardiology Journal Article Cumulative meta-analysis of therapeutic trials for myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/1614465 Meta analysis - Cardiology .   No .
Case Control matching;community based;studies;case control Journal Article The merits of matching in community intervention trials: a cautionary tale. https://www.ncbi.nlm.nih.gov/pubmed/?term=9265698 Matching Community Based Studies .   No .
Case Control Studies Case Control Studies; attributable risk Journal Article Attributable Risk Estimation in Case-Control Studies https://www.ncbi.nlm.nih.gov/pubmed/?term=7925727 Case Control Studies, attributable risk .   No .
Case Control Studies Case Control Studies Journal Article Methodologic problems and standards in case-control research. https://www.ncbi.nlm.nih.gov/pubmed/?term=447778 Case Control Studies .   No .
Case Control Studies Case Control Studies Journal Article Selection of Controls in Case-Control Studies. I. Principles. https://www.ncbi.nlm.nih.gov/pubmed/?term=1595688 Case Control Studies .   No .
Case Control Studies Case Control Studies Online Interactive Course Selection of Controls in Case-Control Studies. II. Types of Controls. https://www.ncbi.nlm.nih.gov/pubmed/?term=1595689 Case Control Studies .   No .
Case Control Studies Case Control Studies Journal Article Selection of Controls in Case-Control Studies. III. Design options. https://www.ncbi.nlm.nih.gov/pubmed/?term=1595690 Case Control Studies .   No .
Case Control Studies Case Control Studies Journal Article The case-control study. A practical review for the clinician. https://www.ncbi.nlm.nih.gov/pubmed/?term=7033572 Case Control Studies .   No .
Case Control Studies matching;community based;studies;case control studies Journal Article The effect of matching on the power of randomized community intervention studies. https://www.ncbi.nlm.nih.gov/pubmed/8456215 Matching Community Based Studies .   No .
Case Control Studies matching;case control studies Journal Article The Comparison of Percentages in Matched Samples https://www.ncbi.nlm.nih.gov/pubmed/?term=14801052 Matching in case control studies .   No .
Case Control Studies matching;case control studies Journal Article he matched pairs design in the case of all-or-none responses. https://www.ncbi.nlm.nih.gov/pubmed/?term=5683874 Matching in case control studies .   No .
Case Control Studies matching;case control studies Journal Article Matching and design efficiency in retrospective studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=5416244 Matching in case control studies .   No .
Case Control Studies matching;case control studies Journal Article Estimating the utility of matching in case-control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6501548 Matching in case control studies .   No .
Case Control Studies matching;case control studies Journal Article A comparison of different matching designs in case-control studies: an empirical example using continuous exposures, continuous confounders and incidence of myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341869 Matching in case control studies .   No .
Case-Control Matching Exact confidence intervals for a proportion; bootstrap; competing risk; case-control matching; survival analysis Software to Download Locally Written SAS Macros http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros The SAS macros below were written and are maintained by Mayo Clinic staff. They contain SAS source code, a brief description of the macro's function and an example of the macro call. 4 SAS No 3
Categorical power;sample size;ordered;categorical;data Journal Article Sample size calculations for ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/8134732 Power/sample size: ordered categorical data .   No .
Categorical Data power sample size;ordered;categorical data Journal Article Sample size calculations for paired or matched ordinal data. https://www.ncbi.nlm.nih.gov/pubmed/9699235 Power Sample Size: Ordered Categorical Data .   No .
Categorical Models Graphics; Linear Models; Structural Equation Models; Categorical Models Website Michael Friendly's personal website http://www.datavis.ca/courses/index.php A variety resources and code for producing graphs and analyzing data 3 SAS, R No 3
Causal Mediation causal mediation; mediator; observational data; counterfactual analysis; Website Causal Mediation Analysis with the CAUSALMED Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1991-2018.pdf Important policy and health care decisions often depend on understanding the direct and indirect (mediated) effects of a treatment on an outcome. For example, does a youth program directly reduce juvenile delinquent behavior, or does it indirectly reduce delinquent behavior by changing the moral and social values of teenagers? Or, for example, is a particular gene directly responsible for causing lung cancer, or does it have an indirect (mediated) effect through its influence on smoking behavior? Causal mediation analysis deals with the mechanisms of causal treatment effects, and it estimates direct and indirect effects. A treatment variable is assumed to have causal effects on an outcome variable through two pathways: a direct pathway and a mediated (indirect) pathway through a mediator variable. This paper introduces the CAUSALMED procedure, new in SAS/STAT® 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework. The paper also defines these causal mediation and related effects in terms of counterfactual outcomes and describes the assumptions that are required for unbiased estimation. Examples illustrate the ideas behind causal mediation analysis and the applications of the CAUSALMED procedure. 4 SAS No 3
Censored And Truncated Regression regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Censoring survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
Certificate certificate; BioInformatics; Informatics Online Interactive Course Public Health Informatics https://www.jhsph.edu/academics/certificate-programs/certificates-for-hopkins-and-non-degree-students/public-health-informatics.html Certificate in BioInformatics offered by JHU/ Bloomberg School of Public Health . Any, SAS Yes .
Certificate Program masters programs; certificate program Online Interactive Course Penn State Online Applied Statistics https://onlinecourses.science.psu.edu/statprogram/ Penn State offers both a certificate and a masters degree program in applied statistics. All coursework can be taken online. Individual courses can also be taken. 2 SAS, R Yes 2
Chi Square contingency table; measure of association; chi square; proportions Journal Article Statistics review 8: Qualitative data - tests of association https://www.ncbi.nlm.nih.gov/pubmed/14975045 This review introduces methods for investigating relationships between two qualitative (categorical) variables. The chi square test of association is described, together with the modifications needed for small samples. The test for trend, in which at least one of the variables is ordinal, is also outlined. Risk measurement is discussed. The calculation of confidence intervals for proportions and differences between proportions are described. Situations in which samples are matched are considered. 2   No .
Chi-Square Test Chi-square test; t-test; linear regression; logistic regression Book OpenIntro Statistics https://www.openintro.org/stat/textbook.php?stat_book=os An introductory textbook and on-line videos about biostatistics, including linear and logistic regression. The PDF is freely downloadable, but a donation is requested. 2 Any No 1
Chi-Squared Chi-squared;Fisher's exact test Journal Article Power of testing proportions in small two-sample studies when sample sizes are equal. https://www.ncbi.nlm.nih.gov/pubmed/?term=8516594 Chi-squared and Fisher's exact test .   No .
Choose Correct Statistical Tests SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Clinical Concern medical practice problems;heart valve;clinical concern Journal Article Twenty-five-year experience with the Björk-Shiley convexoconcave heart valve: a continuing clinical concern. https://www.ncbi.nlm.nih.gov/pubmed/?term=15927993 Medical practice problems .   No .
Clinical Research design;interpretation;clinical research Journal Article Adherence to treatment and health outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8250647 Adherence (or compliance) is the extent to which a person's behavior coincides with medical or health advice. Recent evidence indicates that patients who adhere to treatment, even when that treatment is a placebo, have better health outcomes than poorly adherent patients. Based on this evidence, we now believe that the outcomes of treatment are not solely attributable to the specific action of a drug, but may also depend on other nonspecific therapeutic effects. We consider the implications of these findings for the design and interpretation of clinical research as well as for the care of patients. .   No .
Clinical Research medical literature;evaluation;clinical research Journal Article Critical Evaluation of Clinical Research https://www.ncbi.nlm.nih.gov/pubmed/?term=7811181 Reading Medical Literature .   No .
Clinical Research reading medical literature;clinical research Journal Article Contradicted and initially stronger effects in highly cited clinical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=16014596 Reading Medical Literature .   No .
Clinical Research Methods meta-analyses; large trials;clinical research methods Journal Article Issues in comparisons between meta-analyses and large trials. https://www.ncbi.nlm.nih.gov/pubmed/9546568 meta-analyses and large trials .   No .
Clinical Research Statistics Clinical Research Statistics Online Interactive Course Understanding Clinical Research: Behind the Statistics https://www.coursera.org/learn/clinical-research Per the course website, this course prepared by the University of Cape Town, will meet your interest in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That . Any No .
Clinical Study sample size;clinical study;interpreting results Journal Article Sample size nomograms for interpreting negative clinical studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6881780 In recent years there has been increasing attention to the appropriate interpretation of a clinical study. One special concern has been the difficulty inherent in interpreting studies that were not statistically significant: Was the sample size sufficient to detect a clinically important effect if, in fact, it existed? This concern is further complicated because readers may have differing opinions of what size effect is clinically important. A pair of sample size nomograms has been developed, using common levels of statistical significance, to assist in this interpretation. The nomograms are intended to provide the clinician with a handy and easy-to-use reference for ascertaining whether an apparently negative study has a sample size adequate to detect reliably any difference between treatment groups that the clinician believes is clinically important. Examples are provided to show these principles and the use of the nomograms in interpreting negative studies. .   No .
Clinical Trial methods;analysis;clinical trial;randomization; Journal Article Adjusting for non-compliance and contamination in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9160496 A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive. .   No .
Clinical Trial compliance;clinical trial Journal Article Comparison of the digoxin marker with capsule counting and compliance questionnaire methods for measuring compliance to medication in a clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3322829 During the last quarter of the third year of follow-up in the Helsinki Heart Study, compliance to medication was measured in 1739 patients with digoxin used as a marker substance, capsule counting and a compliance questionnaire. The estimates for good and poor compliers were found to be highly dependent on the method and the cut-off points chosen for the compliance allocation. The methods studied here were more reliable for the detection of poor rather than good compliance. In the poor compliance group, defined with the use of the digoxin marker, there was 39% of subjects who returned less than 5% of their capsule dosage or reported a deviation less than 5%. In the good compliance group, defined by the digoxin marker, only 11.8% of patients either returned or reported a deviation of at least 25% of their dose. The compliance was better when measured by the questionnaire than by capsule counting. The size of the poor compliance group, defined by the use of the digoxin marker, was as large as a group who had returned at least 27% of their capsule dose and a group who had reported a deviation of 11% or more from their dosing schedule. The size of the group allocated to the good compliance category by the use of the digoxin marker was equivalent in size to a group of patients who had returned less than 15% of their prescribed dose or reported a deviation of less than 6% from their prescription. When the strictest criteria for the combination of all three methods were used, 57% of subjects were classified as good and 31% as poor compliers to medication in the third year of the primary prevention trial designed to reduce the incidence of coronary heart disease. .   No .
Clinical Trial clinical trial;randomization;design;variation Journal Article Complexity and contradiction in clinical trial research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3548349 Randomized clinical trials have become the accepted scientific standard for evaluating therapeutic efficacy. Contradictory results from multiple randomized clinical trials on the same topic have been attributed either to methodologic deficiencies in the design of one of the trials or to small sample sizes that did not provide assurance that a meaningful therapeutic difference would be detected. When 36 topics with conflicting results that included over 200 randomized clinical trials in cardiology and gastroenterology were reviewed, it was discovered that results of randomized clinical trials often disagree because the complexity of the randomized clinical trial design and the clinical setting creates inconsistencies and variation in the therapeutic evaluation. Nine methodologic sources of this variation were identified, including six items concerned with the design of the trials, and three items concerned with interpretation. The design issues include eligibility criteria and the selection of study groups, baseline differences in the available population, variability in indications for the principal and concomitant therapies, protocol requirements of the randomized clinical trial, and management of intermediate outcomes. The issues in interpreting the trials include the regulatory effects of treatments, the frailty of double-blinding, and the occurrence of unexpected trial outcomes. The results of this review suggest that pooled analyses of conflicting results of randomized clinical trials (meta-analyses) may be misleading by obscuring important distinctions among trials, and that enhanced flexibility in strategies for data analysis will be needed to ensure the clinical applicability of randomized clinical trial results. .   No .
Clinical Trial clinical trial;planning; Journal Article Planning the size and duration of a clinical trial studying the time to some critical event. https://www.ncbi.nlm.nih.gov/pubmed/?term=4592596 Planning the size and duration of a clinical trial studying the time to some critical event. .   No .
Clinical Trial Missing data;multiple-model;multiple imputation;clinical trial Journal Article Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=23503984 Missing Data .   No .
Clinical Trials clinical trials Book Introduction to Statistical Methods for Clinical Trials (Chapman & Hall/CRC Texts in Statistical Science) https://www.amazon.com/Introduction-Statistical-Methods-Clinical-Chapman/dp/1584880279/ref=sr_1_1?ie=UTF8&qid=1515179174&sr=8-1&keywords=introduction+to+statistical+methods+for+clinical+trials#customerReviews Great introductory book to methodologies in clinical trials. The authors are very accomplished statisticians with many years of clinical trial experience and research. The chapters are very well written and include most of the crucial topics that come up in trial design and development. But it is not an elementary book. Recommend this book because from the methodological viewpoint there is no other book with more depth or broader coverage. 6 Any No 1
Clinical Trials Clinical trials; GEE; logistic regression; correlated binary data Journal Article Sample size and power calculations with correlated binary data https://www.ncbi.nlm.nih.gov/pubmed/11384786 sample size formula for correlated binary data Control Clin Trials. 2001 Jun;22(3):211-27. 5 Any No 4
Clinical Trials design; randomization;clinical trials Journal Article A new design for randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=431682 This paper proposes a new method for planning randomized clinical trials. This method is especially suited to comparison of a best standard or control treatment with an experimental treatment. Patients are allocated into two groups by a random or chance mechanism. Patients in the first group receive standard treatment; those in the second group are asked if they will accept the experimental therapy; if they decline, they receive the best standard treatment. In the analyses of results, all those in the second group, regardless of treatment, are compared with those in the first group. Any loss of statistical efficiency can be overcome by increased numbers. This experimental plan is indeed a randomized clinical trial and has the advantage that, before providing consent, a patient will know whether an experimental treatment is to be used. .   No .
Clinical Trials clinical trials;meta-analysis Journal Article Clinical trials and meta-analysis. What do they do for us? https://www.ncbi.nlm.nih.gov/pubmed/?term=1614470 Clinical trials and meta-analysis. What do they do for us? .   No .
Clinical Trials clinical trials;bayesian;analysis;p value Journal Article Clinical trials and statistical verdicts: probable grounds for appeal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6830080 Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions. .   No .
Clinical Trials clinical trials;ethics Journal Article Clinical trials--are they ethical? https://www.ncbi.nlm.nih.gov/pubmed/?term=1709257 Clinical trials--are they ethical? .   No .
Clinical Trials compliance;clinical trials Journal Article Compliance in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7893432 Compliance in clinical trials. .   No .
Clinical Trials compliance;randomization;design;interpretation;clinical trials Journal Article Compliance with an experimental drug regimen for treatment of asthma: its magnitude, importance, and correlates. https://www.ncbi.nlm.nih.gov/pubmed/?term=6389582 This paper reports on data from a double-blind, randomized controlled study of out-patient use of corticosteroids following an acute asthma attack. Issues related to compliance are examined, including: (1) the extent of non-compliance; (2) impact of non-compliance on interpreting the drug trial results; and (3) correlates of non-compliance. Of the 102 cases enrolled in the study, 25.5% were excluded from analysis because they were lost to follow-up (10.8%) or non-compliers (14.7%). Based on data for compliers, the drugs were found to reduce relapse rates and asthma symptomatology; when non-compliers were included in the analysis, the steroid drug appeared ineffective for reducing relapses and less effective for improving overall illness status. Examination of 24 potential correlates of compliance yielded a few significant associations, and only the "usual habit of compliance" correlation suggests an avenue for future action. The implications of the study findings for design and interpretation of clinical trials, as well as for improved management of chronic diseases, are discussed. .   No .
Clinical Trials clinical trials;confidence intervals; Journal Article Confidence intervals for reporting results of clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3740683 Tests of statistical significance are often used by investigators in reporting the results of clinical research. Although such tests are useful tools, the significance levels are not appropriate indices of the size or importance of differences in outcome between treatments. Lack of "statistical significance" can be misinterpreted in small studies as evidence that no important difference exists. Confidence intervals are important but underused supplements to tests of significance for reporting the results of clinical investigations. Their usefulness is discussed here, and formulas are presented for calculating confidence intervals with types of data commonly found in clinical trials. .   No .
Clinical Trials clinical trials;controversy Journal Article Controversy in counting and attributing events in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=514321 Controversy in counting and attributing events in clinical trials. .   No .
Clinical Trials clinical trials;randomization; Journal Article Deficiencies of clinical trials of alcohol withdrawal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6342448 Eighty-one therapeutic trials of alcohol withdrawal were found that have been published in English since 1954; controls were randomized in 29 (RCTs). Two thousand three hundred thirteen patients were randomized. Variable pretreatment description prevented estimates of delirium tremens and convulsion prevalence, but only four deaths were reported. Endpoints were thus entirely subjective in these moderately ill patients. Protocol quality of the RCTs was graded by a previously developed system for evaluating adequacy of descriptions, blinding, and essential measurements. Mean score obtained was .49 +/- .03 (1 SE). (A perfect paper would score 1.00.) Data presentations and statistical analyses scored .18 +/- .03. There was little evidence of improvement of scores over time. Papers lacked confidence intervals, proper handling of dropouts, and adequate details of side effects. In five RCTs, six comparisons showed that benzodiazepines are clearly superior to placebo (p less than .001), but conclusions about comparisons with other drugs were not possible. In none of eight "negative" comparisons was the probability of a type II error (beta) considered. Discovery of more effective symptomatic agents or methods of reducing the death rate will require more rigid protocols and analyses as well as larger studies to allow the use of more critical endpoints such as occurrence of delirium tremens, convulsions, or death. .   No .
Clinical Trials clinical trials;compliance Journal Article Facilitated analysis of data on drug regimen compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=9257419 Drug actions depend on dose and intervals between doses, in ways that are drug-specific and sometimes complex, complicating the use of drug dosing histories as an explanatory variable in the analysis of clinical trials. We describe a spread-sheet method for conveniently displaying patients' drug dosing histories, to facilitate identification of dosing correlates of clinically important events. .   No .
Clinical Trials clinical trials;randomization Journal Article How should clinicians interpret clinical trials? https://www.ncbi.nlm.nih.gov/pubmed/?term=7585780 Given the rapid evolution of cardiovascular medicine, clinicians must sift through an enormous array of information about new therapies in order to determine how best to treat patients with ischemic heart disease. They should first consider the evidence from randomized clinical trials, because these trials eliminate bias and permit broad statistical analyses. If randomized clinical trial data are not available, next in order of the strength of their evidence are observational studies, historically controlled studies, case series, and case reports. Clinicians must additionally ascertain that an investigation has the elements of good design, including a clear question, adequate sample size, appropriate inclusion and exclusion criteria, evidence that the right amount of data was collected carefully, and allowances in the analyses for patients taking multiple therapies and randomized into several clinical trials. .   No .
Clinical Trials clinical trials;confidence intervals Journal Article Inadequate size of 'negative' clinical trials in dermatology. https://www.ncbi.nlm.nih.gov/pubmed/?term=8471517 Use of confidence intervals to summarize clinical trial findings, so that readers can quickly decide whether clinically important treatment effects are plausible. .   No .
Clinical Trials clinical trials Journal Article Intention-to-treat analysis and the goals of clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7828382%5Buid%5D Intention-to-treat analysis and the goals of clinical trials. .   No .
Clinical Trials analysis;clinical trials;randomization;intent-to-treat; Journal Article Intention-to-treat vs. on-treatment analyses of clinical trial data: experience from a study of pyrimethamine in the primary prophylaxis of toxoplasmosis in HIV-infected patients. ANRS 005/ACTG 154 Trial Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=9620807 Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. This paper presents several approaches used for analyzing data of a recent trial and the difficulties encountered in interpreting the results of each approach. Although exploratory analyses may yield clinically relevant information and useful clarifications in the evaluation of treatments, intention-to-treat remains the only interpretable analysis of clinical trials. .   No .
Clinical Trials clinical trials; misconduct Journal Article Problems in clinical trials go far beyond misconduct. https://www.ncbi.nlm.nih.gov/pubmed/?term=8202708 Problems in clinical trials go far beyond misconduct. .   No .
Clinical Trials compliance;clinical trials; interpretation Journal Article Role of patient compliance in clinical pharmacokinetics. A review of recent research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7988102 Until 1986 to 1987, the estimation of patient compliance with prescribed drug regimens in ambulatory care relied on methods that were biased either by their subjectivity or by the improvement in compliance that commonly occurs during the day or two prior to a scheduled examination, so called 'white-coat compliance'. In 1986 to 1987, 2 objective methods were developed: electronic monitoring and low-dose, slow-turnover chemical markers (digoxin or phenobarbital [phenobarbitone]) incorporated into dosage forms. While neither method is without limitations, both have enabled major advances in the understanding of patients' compliance with dosage regimens and, thus, the spectrum of drug exposure in ambulatory care. The new methods have also triggered not only a revival of interest in patient compliance and its determinants, but also new statistical approaches to interpreting the clinical correlates of widely variable drug administration, and thus drug exposure, in drug trials. The marker methods prove dose ingestion during the 3 to 7 days prior to blood sampling, but do not reveal the timing of doses. The electronic monitoring methods, i.e. time and date-stamping microcircuitry incorporated into drug packages, provide a continuous record of timing of presumptive doses throughout periods of many months, but do not prove dose ingestion. The electronic record has been judged robust enough to detect certain types of investigator fraud, and to support modelling projections of the complete time course of the plasma drug concentration during a trial. Both marker and electronic methods show that the predominant errors are those of omission, i.e. delays or omissions of scheduled doses. Patient interviews, diaries, and counts of returned, untaken doses have been shown by both marker and electronic monitoring methods to consistently and substantially to overestimate compliance. Monitoring of plasma drug concentrations also overestimates compliance, because white-coat compliance is prevalent, and the pharmacokinetic turnover of most drugs is rapid enough that measured concentrations of drug in plasma reflect only drug administration during the period of white-coat compliance. Thus, compliance is a great deal poorer in clinical trials than has been revealed by the older methods. The long-standing underestimation of poor compliance in drug trials has many implications for the interpretation of drug trials, for optimal dose estimation, for the interpretation of failed drug therapy, and for accurate labelling of prescription drugs. .   No .
Clinical Trials sample size;clinical trials Journal Article Sample sizes for phase II and phase III clinical trials: an integrated approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=3787000 In this paper the following problem of clinical research is explored. Several potential new treatments are available for use against a certain disease. These are evaluated in a series of pilot studies which will constitute phase II clinical trials. The most promising will then be compared with a standard treatment in a phase III trial. Of interest will be the number of patients needed for the complete research programme, the proportions of these that should be involved in each phase, and the number of treatments which should be tried. Optimal strategies are found which maximize the probability that the overall programme identifies a treatment which is significantly better than the standard. . Stata No .
Clinical Trials clinical trials;analysis;repeated measures; confidence intervals Journal Article Statistical problems in the reporting of clinical trials. A survey of three medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=3614286 Reports of clinical trials often contain a wealth of data comparing treatments. This can lead to problems in interpretation, particularly when significance testing is used extensively. We examined 45 reports of comparative trials published in the British Medical Journal, the Lancet, or the New England Journal of Medicine to illustrate these statistical problems. The issues we considered included the analysis of multiple end points, the analysis of repeated measurements over time, subgroup analyses, trials of multiple treatments, and the overall number of significance tests in a trial report. Interpretation of large amounts of data is complicated by the common failure to specify in advance the intended size of a trial or statistical stopping rules for interim analyses. In addition, summaries or abstracts of trials tend to emphasize the more statistically significant end points. Overall, the reporting of clinical trials appears to be biased toward an exaggeration of treatment differences. Trials should have a clearer predefined policy for data analysis and reporting. In particular, a limited number of primary treatment comparisons should be specified in advance. The overuse of arbitrary significance levels (for example, P less than 0.05) is detrimental to good scientific reporting, and more emphasis should be given to the magnitude of treatment differences and to estimation methods such as confidence intervals. .   No .
Clinical Trials clinical trials;confidence intervals;sample size Journal Article The case for confidence intervals in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8001360 A statistical wit once remarked that researchers often pose the wrong question and then proceed to answer that question incorrectly. The question that researchers intend to ask is whether or not a treatment effect is clinically significant. The question that is typically asked, however, is whether or not the treatment effect is statistically significant--a question that may be only marginally related to the issue of clinical impact. Similarly, the response, in the form of a p value, is typically assumed to reflect clinical significance but in fact reflects statistical significance. In an attempt to address this problem the medical literature over the past decade has been moving away from tests of significance and toward the use of confidence intervals. Concretely, study reports are moving away from "the difference was significant with a p value under 0.01" and toward "the one-year survival rate was increased by 20 percentage points with a 95% confidence interval of 15 to 24 percentage points." By focusing on what the effect is rather than on what the effect is not confidence intervals offer an appropriate framework for reporting the results of clinical trials. This paper offers a non-technical introduction to confidence intervals, shows how the confidence intervals framework offers advantages over hypothesis testing, and highlights some of the controversy that has developed around the application of this method. Additionally, we make the argument that studies which will be reported in terms of confidence intervals should similarly be planned with reference to confidence intervals. The sample size should be set to ensure that the estimates of effect size will be reported not only with adequate power but also with appropriate precision. .   No .
Clinical Trials compliance;sample size;clinical trials Journal Article The effect of poor compliance and treatment side effects on sample size requirements in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7951277%5Buid%5D Treatment side effects and associated noncompliance have methodological implications vital to the testing of new drugs. In this paper, we quantify the impact of these factors on sample size requirements in clinical trials. In the Lipid Research Clinics Trial, side effects caused treatment group compliance (50.8%) to be lower than placebo compliance (67.3%). Cholesterol reduction among treatment noncompliers was 35.2% of the reduction among compliers. Had treatment group compliance been as high as placebo compliance, 41% fewer patients would have been required to achieve the same statistical power and an expected 31% more coronary events would have been prevented. We conclude: Because they discourage patient compliance, treatment side effects can (1) cause large sample size increases, (2) lead to underestimates of true efficacy, and (3) contribute to potentially invalid negative conclusions in clinical trials. The impact of side effects goes well beyond the complications and patient discomforts with which they are associated. .   No .
Clinical Trials randomized control trials;sample size;clinical trials Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. Survey of 71 "negative" trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Seventy-one "negative" randomized control trials were re-examined to determine if the investigators had studied large enough samples to give a high probability (greater than 0.90) of detecting a 25 per cent and 50 per cent therapeutic improvement in the response. Sixty-seven of the trials had a greater than 10 per cent risk of missing a true 25 per cent therapeutic improvement, and with the same risk, 50 of the trials could have missed a 50 per cent improvement. Estimates of 90 per cent confidence intervals for the true improvement in each trial showed that in 57 of these "negative" trials, a potential 25 per cent improvement was possible, and 34 of the trials showed a potential 50 per cent improvement. Many of the therapies labeled as "no different from control" in trials using inadequate samples have not received a fair test. Concern for the probability of missing an important therapeutic improvement because of small sample sizes deserves more attention in the planning of clinical trials. .   No .
Clinical Trials Clinical Trials; Effect on Medical Practice Journal Article Bias in Analytic Research https://www.ncbi.nlm.nih.gov/pubmed/?term=447779 Clinical Trials: Effect on Medical Practice .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Dynamic balanced randomization for clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8134737 Clinical Trials: Randomization Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Empirical Evidence of Bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7823387 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Ensuring Balanced Distribution of Prognostic Factors in Treatment Outcome Research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7723001 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Effect on Medical Practice Journal Article Evidence Favoring The Use of Anticoagulants in the Hospital Phase of Acute Myocardial Infarction https://www.ncbi.nlm.nih.gov/pubmed/?term=909566 Clinical Trials: Effect on Medical Practice .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Generation of allocation sequences in randomized trials: chance, not choice. https://www.ncbi.nlm.nih.gov/pubmed/?term=11853818 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article How study design affects outcomes in comparisons of therapy. II: Surgical. https://www.ncbi.nlm.nih.gov/pubmed/?term=2727469 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article How to study design affects outcomes in comparisons of therapy. I: Medical https://www.ncbi.nlm.nih.gov/pubmed/?term=2727468 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Randomized versus Historical Controls for Clnical Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7058834 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Reporting Randomized Controlled Trials. An Experiment and a call for responses From Readers. https://www.ncbi.nlm.nih.gov/pubmed/?term=7897791 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Subverting Randomization in Controlled Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7474192 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Ethics;Fraud;Monitoring Journal Article The dangers of inferring treatment effects from observational data: a case study in HIV infection https://www.ncbi.nlm.nih.gov/pubmed/?term=11943438 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Clinical Trials Clinical Trials;Ethics;Fraud;Monitoring Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups ;Adaptive Sampling Journal Article The randomization and stratification of patients to clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=4612056 Clinical Trials: Randomization/Control Groups & Adaptive Sampling .   No .
Clinical Trials Clinical Trials;Randomization;Control Groups Journal Article Treatment allocation Methods in clinical trials: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=3895341 Clinical Trials: Randomization/Control Groups .   No .
Clinical Trials Clinical Trials;Ethics;Fraud;Monitoring Journal Article When Was a 'Negative' Clinical Trail Big Enough? How Many Patients You Needed Depends on What You Found. https://www.ncbi.nlm.nih.gov/pubmed/?term=3985731 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Clinical Trials QC;clinical trials Journal Article An examination of the efficiency of some quality assurance methods commonly employed in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=pmid%3A+2345830 QC in clinical trials .   No .
Clinical Trials qc;clinical trials Journal Article Guidelines for quality assurance in multicenter trials: a position paper. https://www.ncbi.nlm.nih.gov/pubmed/9741868 QC in clinical trials .   No .
Clinical Trials qc;clinical trials Journal Article The other side of clinical trial monitoring; assuring data quality and procedural adherence. https://www.ncbi.nlm.nih.gov/pubmed/?term=17170037 QC in clinical trials .   No .
Clinical Trials qc;clinical trials Journal Article Double data entry: what value, what price? https://www.ncbi.nlm.nih.gov/pubmed/9492966 QC in clinical trials .   No .
Clinical Trials qc;clinical trials Journal Article Short report: Piloting paperless data entry for clinical research in Africa. https://www.ncbi.nlm.nih.gov/pubmed/?term=15772326 QC in clinical trials .   No .
Clinical Trials publication bias;clinical trials Journal Article Publication bias and clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3442991 Publication bias .   No .
Clinical Trials group;sequential;tests;data;clinical trials Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Test .   No .
Clinical Trials group;sequential;tests;clinical trials;data Journal Article Group sequential testing in clinical trials with multivariate observations: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8122047 Group Sequential Tests .   No .
Clinical Trials Design Cancer;Clinical Trials Design Journal Article Optimal two-stage designs for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702835 Cancer: Clinical Trials Design .   No .
Cluster Randomization cluster randomization Journal Article Contamination in trials: is cluster randomisation the answer? https://www.ncbi.nlm.nih.gov/pubmed/?term=11159665 Contamination in trials: is cluster randomisation the answer? .   No .
Cluster Randomization cluster randomization; sample size Journal Article Sample size calculations for cluster randomised controlled trials with a fixed number of clusters. https://www.ncbi.nlm.nih.gov/pubmed/?term=21718530 Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. .   No .
Cluster Randomization Trials cluster randomization trials;ethics Journal Article Pitfalls of and controversies in cluster randomization trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998805 It is now well known that standard statistical procedures become invalidated when applied to cluster randomized trials in which the unit of inference is the individual. A resulting consequence is that researchers conducting such trials are faced with a multitude of design choices, including selection of the primary unit of inference, the degree to which clusters should be matched or stratified by prognostic factors at baseline, and decisions related to cluster subsampling. Moreover, application of ethical principles developed for individually randomized trials may also require modification. We discuss several topics related to these issues, with emphasis on the choices that must be made in the planning stages of a trial and on some potential pitfalls to be avoided. .   No .
Cluster Randomized Trials cluster randomized trials;design;sample size Journal Article Methods for sample size determination in cluster randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=26174515 The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. .   No .
Clustered Trials clustered trials; hierarchical models; power analysis Interactive Program Power analysis for group randomized trials https://ssc.researchmethodsresources.nih.gov/ssc/ NIH website about clustered randomized trials 2 Any No 1
Cochran-Mantel-Haenszel Test Cochran-Mantel-Haenszel test; power Website Introduction to the Cochran-Mantel-Haenszel Test https://cran.r-project.org/web/packages/samplesizeCMH/vignettes/samplesizeCMH-introduction.html The Cochran-Mantel-Haenszel test (CMH) is an inferential test for the association between two binary variables, while controlling for a third confounding nominal variable. A good introduction to the test along with R code for various aspects of the test including sample size/power calculations. 3 R No 3
Community paired;community;designs;sample;size Journal Article Breaking the matches in a paired t-test for community interventions when the number of pairs is small. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481187 Paired Community Designs Sample Size .   No .
Community paired;community;designs;sample size Journal Article Planning for the appropriate analysis in school-based drug-use prevention studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=2212183 Paired Community Designs Sample Size .   No .
Community paired;community;designs;sample;size Journal Article Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=2066748 Paired Community Designs Sample Size .   No .
Community paired;community;design;sample size Journal Article Aspects of statistical design for the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/?term=1315664 Paired Community Design Sample Size .   No .
Community paired;community;designs;sample size Journal Article A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. https://www.ncbi.nlm.nih.gov/pubmed/?term=2084005 Paired Community Design Sample Size .   No .
Community Based matching;community based;studies Journal Article The efficiency of the matched-pairs design of the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/9129857 Matching Community Based Studies .   No .
Community Based matching;community based;studies;case control Journal Article The merits of matching in community intervention trials: a cautionary tale. https://www.ncbi.nlm.nih.gov/pubmed/?term=9265698 Matching Community Based Studies .   No .
Community Based matching;community based;studies;case control studies Journal Article The effect of matching on the power of randomized community intervention studies. https://www.ncbi.nlm.nih.gov/pubmed/8456215 Matching Community Based Studies .   No .
Comparisons multiple;comparisons;data;analysis Journal Article Gatekeeping Strategies for Avoiding False-Positive Results in Clinical Trials With Many Comparisons. https://www.ncbi.nlm.nih.gov/pubmed/?term=29049572 Multiple Comparisons .   No .
Comparisons multiple;comparisons;data;analysis Journal Article P-value interpretation and alpha allocation in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/9708870 Multiple Comparisons .   No .
Comparisons multiple;comparisons;data;analysis Journal Article Multiple comparisons and related issues in the interpretation of epidemiologic data https://www.ncbi.nlm.nih.gov/pubmed/?term=7572970 Multiple Comparisons .   No .
Comparisons multiple;comparisons;data;analysis Journal Article Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8629727 Multiple Comparisons .   No .
Comparisons multiple;comparisons;data;analysis Journal Article Impact of multiple comparisons in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3661589 Multiple Comparisons .   No .
Comparisons multiple;comparisons;data;analysis Journal Article Multiple hypothesis tests in multiple investigations. https://www.ncbi.nlm.nih.gov/pubmed/?term=7792449 Multiple Comparisons .   No .
Competing Risk Exact confidence intervals for a proportion; bootstrap; competing risk; case-control matching; survival analysis Software to Download Locally Written SAS Macros http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros The SAS macros below were written and are maintained by Mayo Clinic staff. They contain SAS source code, a brief description of the macro's function and an example of the macro call. 4 SAS No 3
Competing Risks survival data analysis;competing risks;Kaplan-Meier;method; Journal Article A note on competing risks in survival data analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=15305188 Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events. .   No .
Competing Risks probability;competing risks;Kaplan-Meier; Journal Article Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. https://www.ncbi.nlm.nih.gov/pubmed/?term=10204198 A topic that has received attention in both the statistical and medical literature is the estimation of the probability of failure for endpoints that are subject to competing risks. Despite this, it is not uncommon to see the complement of the Kaplan-Meier estimate used in this setting and interpreted as the probability of failure. If one desires an estimate that can be interpreted in this way, however, the cumulative incidence estimate is the appropriate tool to use in such situations. We believe the more commonly seen representations of the Kaplan-Meier estimate and the cumulative incidence estimate do not lend themselves to easy explanation and understanding of this interpretation. We present, therefore, a representation of each estimate in a manner not ordinarily seen, each representation utilizing the concept of censored observations being 'redistributed to the right.' We feel these allow a more intuitive understanding of each estimate and therefore an appreciation of why the Kaplan-Meier method is inappropriate for estimation purposes in the presence of competing risks, while the cumulative incidence estimate is appropriate. .   No .
Competing Risks Kaplan-Meier; probability;competing risks Journal Article Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? https://www.ncbi.nlm.nih.gov/pubmed/?term=8516591 In the context of competing risks the Kaplan-Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two-sample test statistics are also presented. .   No .
Competing Risks hazard function; competing risks; survival analysis Website Cause-Specific Analysis of Competing Risks Using the PHREG Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2159-2018.pdf Competing-risks analysis extends the capabilities of conventional survival analysis to deal with time-to-event data that have multiple causes of failure. Two regression modeling approaches can be used: one focuses on the cumulative incidence function (CIF) from a particular cause, and the other focuses on the cause-specific hazard function. These two quantities, unlike the hazard function and the survival function in conventional survival settings, are not connected through a simple one-to-one relationship. The Fine and Gray model extends the Cox model to analyze the cumulative incidence function but is often mistakenly assumed to be the only modeling technique available. The cause-specific approach that simultaneously models all the cause-specific hazard functions offers a more natural interpretation. SAS/STAT 14.3 includes updates to the PHREG procedure to perform the cause-specific analysis of competing risks. This paper describes how cause-specific hazard regression works and compares it to the Fine and Gray method. Examples illustrate how to interpret the models appropriately and how to obtain predicted cumulative incidence function. 4 SAS No 3
Compliance compliance Journal Article Accuracy of indirect measures of medication compliance in hypertension. https://www.ncbi.nlm.nih.gov/pubmed/?term=3846318 The purpose of this study was to identify the most accurate indirect measure of medication compliance in primary hypertension through comparison with a recently developed direct measure of the antihypertensive agent, hydrochlorothiazide. A convenience sample of 40 subjects was seen by the investigator twice in an office setting and once in their homes. Data were collected by an interview schedule, blood pressure measurement, pill counts, urine analysis, and hospital record review. Patient interview was the most sensitive and accurate measure of compliance; this measure correctly classified 85% of patients as to compliant or noncompliant. .   No .
Compliance compliance Journal Article Adherence to cholesterol-lowering diets. https://www.ncbi.nlm.nih.gov/pubmed/?term=1599339 Adherence to cholesterol-lowering diets. .   No .
Compliance compliance Journal Article Adherence to oral tamoxifen: a comparison of patient self-report, pill counts, and microelectronic monitoring. https://www.ncbi.nlm.nih.gov/pubmed/?term=8501505 Recent innovations allow the integration of microelectronics into drug packaging, providing a continuous record of the interactions of the patient with the drug package. We hypothesized that adherence to oral tamoxifen, as measured by a pressure-activated microelectronic monitoring device, would be significantly discrepant from traditional measures of patient adherence, ie, patient self-report (SR) and pill counts (PCs). CONCLUSION: Microelectronic adherence monitoring provides both confirmatory and complimentary data regarding adherence behavior, while also allowing for the evaluation of patterns of nonadherence. Patient SRs and PCs likely overestimate the degree to which patients adhere to their tamoxifen regimen. .   No .
Compliance compliance;biostatistics Journal Article Clinical biostatistics. XXX. Biostatistical problems in 'compliance bias'. https://www.ncbi.nlm.nih.gov/pubmed/?term=4426153 Clinical biostatistics. XXX. Biostatistical problems in 'compliance bias'. .   No .
Compliance compliance;clinical trial Journal Article Comparison of the digoxin marker with capsule counting and compliance questionnaire methods for measuring compliance to medication in a clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3322829 During the last quarter of the third year of follow-up in the Helsinki Heart Study, compliance to medication was measured in 1739 patients with digoxin used as a marker substance, capsule counting and a compliance questionnaire. The estimates for good and poor compliers were found to be highly dependent on the method and the cut-off points chosen for the compliance allocation. The methods studied here were more reliable for the detection of poor rather than good compliance. In the poor compliance group, defined with the use of the digoxin marker, there was 39% of subjects who returned less than 5% of their capsule dosage or reported a deviation less than 5%. In the good compliance group, defined by the digoxin marker, only 11.8% of patients either returned or reported a deviation of at least 25% of their dose. The compliance was better when measured by the questionnaire than by capsule counting. The size of the poor compliance group, defined by the use of the digoxin marker, was as large as a group who had returned at least 27% of their capsule dose and a group who had reported a deviation of 11% or more from their dosing schedule. The size of the group allocated to the good compliance category by the use of the digoxin marker was equivalent in size to a group of patients who had returned less than 15% of their prescribed dose or reported a deviation of less than 6% from their prescription. When the strictest criteria for the combination of all three methods were used, 57% of subjects were classified as good and 31% as poor compliers to medication in the third year of the primary prevention trial designed to reduce the incidence of coronary heart disease. .   No .
Compliance compliance Journal Article Compliance declines between clinic visits. https://www.ncbi.nlm.nih.gov/pubmed/?term=2369248 Adherence to prescribed drug dosing regimens declined substantially during the interval between clinic visits and drug level tests. Using microelectronic monitors to observe pill-taking habits, 20 patients averaged 88% compliance before and 86% compliance after the visit, but this dropped to 67% compliance a month later. These data indicate that spot drug levels do not represent long-term "steady-state" drug serum concentrations. .   No .
Compliance compliance;clinical trials Journal Article Compliance in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7893432 Compliance in clinical trials. .   No .
Compliance compliance Journal Article Compliance to self-monitoring of blood glucose: a marked-item technique compared with self-report. https://www.ncbi.nlm.nih.gov/pubmed/?term=4053931 This study compared subjects' self-reported rates of compliance to self-monitoring of blood glucose (SMBG) with an objective measure based on a "marked-item" technique. We followed 25 obese patients with type II diabetes who were participating in a behavorial weight control program and monitoring their blood glucose with Chemstrips bG (Bio-Dynamics, Inc., Indianapolis, Indiana). Subjects' self-report significantly overestimated actual compliance as assessed by the marked-item technique. Moreover, the self-report measure failed to identify 35-45% of the noncompliant patients. Compliance decreased steadily over the course of the 37-wk program. Accuracy of SMBG was less problematic than compliance; 85% of patients were able to read Chemstrips bG within 20% of actual blood sugar, and the average blood sugar reading obtained from 2 mo of SMBG correlated highly (r = 0.78, P less than 0.01) with HbA1. Our data suggest that objective measures such as the marked-item technique described in this article should be used to assess compliance to SMBG and behavioral strategies to improve compliance should be developed. .   No .
Compliance compliance;randomization;design;interpretation;clinical trials Journal Article Compliance with an experimental drug regimen for treatment of asthma: its magnitude, importance, and correlates. https://www.ncbi.nlm.nih.gov/pubmed/?term=6389582 This paper reports on data from a double-blind, randomized controlled study of out-patient use of corticosteroids following an acute asthma attack. Issues related to compliance are examined, including: (1) the extent of non-compliance; (2) impact of non-compliance on interpreting the drug trial results; and (3) correlates of non-compliance. Of the 102 cases enrolled in the study, 25.5% were excluded from analysis because they were lost to follow-up (10.8%) or non-compliers (14.7%). Based on data for compliers, the drugs were found to reduce relapse rates and asthma symptomatology; when non-compliers were included in the analysis, the steroid drug appeared ineffective for reducing relapses and less effective for improving overall illness status. Examination of 24 potential correlates of compliance yielded a few significant associations, and only the "usual habit of compliance" correlation suggests an avenue for future action. The implications of the study findings for design and interpretation of clinical trials, as well as for improved management of chronic diseases, are discussed. .   No .
Compliance compliance Journal Article Compliance with contraceptives and other treatments. https://www.ncbi.nlm.nih.gov/pubmed/?term=8752223 To define some of the issues unique to compliance with oral contraceptives, and some comparisons with other preventive and therapeutic treatments. CONCLUSIONS: Studies of a variety of medical disorders have shown that no consequence is so severe that all patients can be assumed to comply with the prescribed treatment plan. Inadequate compliance often diminishes treatment efficacy, which suggests the need for alternative methods that do not require daily compliance. .   No .
Compliance compliance Journal Article Compliance with topical pilocarpine treatment. https://www.ncbi.nlm.nih.gov/pubmed/?term=3706455 Using an unobtrusive eyedrop medication monitor, we measured compliance with topical pilocarpine treatment in a sample of 184 patients. The eyedrop monitor recorded electronically the date and time of each pilocarpine administration over a four- to six-week period. The subjects administered a mean +/- S.D. of 76.0% +/- 24.3% of the prescribed pilocarpine doses. Eleven patients (6%) took less than one quarter and 28 patients (15.2%) took less than one half of the prescribed administrations. In contrast, when the subjects were interviewed they reported taking a mean +/- S.D. of 97.1% +/- 5.9% of the prescribed pilocarpine doses. As determined by the monitor, 45 patients (24.5%) had at least one day per month with no administrations of pilocarpine; 56 subjects (30.4%) compressed the doses during the daytime hours, leaving an interval between the night dose and the morning dose the next day of 12 hours or more. The rate of compliance was significantly higher (P less than .0001) in the 24-hour period preceding the return appointment than in the entire observation period. .   No .
Compliance compliance;bias Journal Article Coronary heart disease and estrogen replacement therapy. Can compliance bias explain the results of observational studies? https://www.ncbi.nlm.nih.gov/pubmed/?term=8205277 The overall risk/benefit of estrogen replacement therapy (ERT) is strongly dependent on assumptions about the effect of ERT on coronary heart disease (CHD). The belief that ERT causes a substantial reduction in the risk of CHD is widespread. The studies that provide support for this belief are all nonexperimental ones. Three analyses of data from two randomized clinical trials of drug treatments for CHD have examined the association of compliance with total mortality in persons who complied with the taking of placebo. In these analyses, compliance with the taking of a placebo was associated with a reduction in overall mortality of the same magnitude as the reduction in the risk of CHD in users of ERT. The benefit of compliance with placebo was not reduced by adjustment for a large number of variables, both medical and sociodemographic, that might affect mortality. Users of ERT are compliers, and the possibility that compliance bias may account for some of the apparent benefit of ERT for CHD must be taken seriously. .   No .
Compliance noncompliance;compliance Journal Article Detection methods and strategies for improving medication compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=1928147 The reliability of compliance detection methods and practical strategies for improving patient compliance with drug therapy are reviewed. Detection of noncompliance is a necessary prerequisite for adequate treatment. Noncompliance can be detected by indirect methods (e.g., self-report, interview, therapeutic outcome, pill count, computerized compliance monitors) or direct methods (e.g., biologic markers, tracer compounds, biologic assay of body fluids). In general, the direct methods of detection have a higher sensitivity and specificity than the indirect methods. Computerized compliance monitors are the most recent and reliable of the indirect-detection methods. Strategies for improving compliance involve identification of risk factors for non-compliance; development, with the patient's participation, of an individualized treatment plan that simplifies the regimen as much as possible; education of the patient, including information about his or her illness, instructions on how to take the prescribed medication correctly, and an explanation of the benefits and possible adverse effects of the therapy; and, if necessary, use of compliance aids such as medication calendars, special containers, caps, and dispensing systems, or compliance packaging. The patient should be taught to monitor his or her own treatment regimen. Follow-up monitoring by health-care professionals, including pharmacists, will also help ensure that the patient is complying with the treatment regimen. Health-care practitioners need to understand factors that contribute to noncompliance and to use effective methods for assessing and monitoring compliance in conjunction with strategies aimed at increasing compliant behavior. .   No .
Compliance compliance Journal Article Drug compliance in therapeutic trials: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=9620809 Because poor compliance introduces a major risk of bias in the interpretation of the results of a therapeutic trial, it is an important element to consider. The analysis should incorporate compliance as a specific variable in order to help test the robustness of the data. Compliance constitutes by itself a specific outcome measure. Compliance should be an integral part of study reports and publications, but it is frequently not discussed. .   No .
Compliance clinical trials;compliance Journal Article Facilitated analysis of data on drug regimen compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=9257419 Drug actions depend on dose and intervals between doses, in ways that are drug-specific and sometimes complex, complicating the use of drug dosing histories as an explanatory variable in the analysis of clinical trials. We describe a spread-sheet method for conveniently displaying patients' drug dosing histories, to facilitate identification of dosing correlates of clinically important events. .   No .
Compliance compliance Journal Article How often is medication taken as prescribed? A novel assessment technique. https://www.ncbi.nlm.nih.gov/pubmed/?term=2716163 The evaluation of the efficacy of medication is confounded when patients do not adhere to prescribed regimens. Overdosing, underdosing, and erratic dosing intervals can diminish drug action or cause adverse effects. Using a new method with epilepsy as a model, we assessed compliance with long-term medications among newly treated and long-term patients. Medication Event Monitor Systems (Aprex Corporation, Fremont, Calif) are standard pill bottles with micro-processors in the cap to record every bottle opening as a presumptive dose. Compliance rates averaged 76% during 3428 days observed: 87% of the once daily, 81% of the twice daily, 77% of the three times a day, and 39% of the four times a day dosages were taken as prescribed. Coefficients of variation of drug serum concentrations had no significant relationship to compliance rates. Pill counts overestimated compliance increasingly as compliance with the prescribed regimen declined. Neither drug serum concentrations nor pill counts would have identified the frequency of missed doses that were revealed with continuous dose observations. .   No .
Compliance compliance; Journal Article Impact of medication nonadherence on coronary heart disease outcomes. A critical review. https://www.ncbi.nlm.nih.gov/pubmed/?term=9308504 A critical review of published literature was performed to assess the impact of medication adherence on morbidity and mortality among patients with or at risk for coronary artery disease and congestive heart failure. Twenty-one original research articles that met our inclusion criteria and related medication adherence to morbidity and mortality are summarized. No clinical trials that specifically tested the impact of a compliance-enhancing intervention on outcome in coronary heart disease were identified. Among 12 studies that compared hospitalization rates and mortality between adherers and nonadherers, 7 showed a significant relationship between medication adherence and outcomes. Three studies showed that adherence to placebo was associated with improved outcomes, suggesting that adherent behavior may be a marker of better prognosis or confers a protective effect on patients with coronary heart disease. Further study is necessary to determine whether adherent behavior can be taught and whether compliance-enhancing strategies improve outcomes in coronary heart disease. .   No .
Compliance compliance; Journal Article Increasing patient compliance with prescriptions. https://www.ncbi.nlm.nih.gov/pubmed/?term=7143651 In this review, we address the problem of patient noncompliance with taking prescribed medication. .   No .
Compliance compliance Journal Article Medication compliance as a feature in drug development. https://www.ncbi.nlm.nih.gov/pubmed/?term=9131261 Well-designed clinical trials maximize the information that can be obtained regarding the clinical pharmacology of a drug and, in turn, can streamline and enhance the drug development process. Considerations in the design of clinical trials must therefore be expanded to include appropriate methods to measure compliance, sufficient frequency of monitoring to allow the time course of response to be mapped, and the use of statistically valid methods of data analysis. .   No .
Compliance compliance Journal Article Measurement of medication compliance in a clinical setting. Comparison of three methods in patients prescribed digoxin. https://www.ncbi.nlm.nih.gov/pubmed/?term=443967 Medication compliance is an important medical process, and useful methods are needed to measure compliance in clinical practice. In clinical practice, interview may be the most useful method of measuring medication compliance. .   No .
Compliance compliance;randomized clinical trials; Journal Article Measurement of patient compliance and the interpretation of randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=1838332 The aim of this review is to demonstrate why the management of compliance, although not an explicit feature of the rules of Good Clinical Practice, is essential to the successful conduct of clinical trials and in correct interpretation of the results. .   No .
Compliance meta-analysis;compliance Journal Article Meta-analysis adjusting for compliance: the example of screening for breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=1432006 Randomized controlled trials are usually analysed by the group to which the patient was randomized, i.e. by "intention-to-treat", regardless of the degree of compliance. However, the "explanatory" effect, i.e. the effect that would occur if we had 100% compliance, is often of interest. This "explanatory" effect is diluted by poor compliance, and hence meta-analyses should ideally avoid both the heterogeneity of effect due to variation in compliance rates among studies, and the undeserved weight given to trials with poor compliance. Newcombe's deattenuation method, which adjusts estimates for the degree of compliance, is extended and applied to a meta-analysis of the five reported randomized controlled trials of mammographic screening. Compliance with screening varied across studies: from 61 to 93% assigned to screening had one or more mammograms. The adjusted estimate of the reduction in breast cancer mortality at 9 years follow-up is 0.37 (95% confidence interval: 0.21, 0.49). .   No .
Compliance compliance;methodology Journal Article Methods in assessing drug compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=6588737 Methods and problems in assessing drug compliance are related to the selection of study sample and observation period as well as to methods used to measure patient behaviour in taking medications. Patients under treatment with a certain drug regimen are different from the patients for whom that regimen was originally prescribed. Medication compliance during short periods of time, such as 1-2 weeks before or after a visit to the clinic, is likely to be different from that found over longer periods of time. Several studies indicate that estimates by clinical staff are no more accurate than chance selections in determining medication compliance. Patient interviews have usually identified 25-50% of noncompliant patients, but interview data on spacing between doses seem to be more accurate. Pill counts are useful in assessing drug compliance, although compliance may sometimes be overestimated. Medication monitors provide more detailed information on patient behaviour in taking medications. .   No .
Compliance compliance; Journal Article Minimal doses of digoxin: a new marker for compliance to medication. https://www.ncbi.nlm.nih.gov/pubmed/?term=3322828 A direct and objective method of measuring compliance to medication is presented. Digoxin is used as a marker in capsules of either gemfibrozil or placebo with a minimal dose of 4.4 micrograms twice a day. The results therefore suggest that the digoxin marker represents a particularly effective method to study compliance to medication during such long-lasting clinical investigations. .   No .
Compliance compliance Journal Article On white-coat effects and the electronic monitoring of compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=2369237 On white-coat effects and the electronic monitoring of compliance. .   No .
Compliance compliance;methodology;interpreting results Journal Article Patient compliance and medical research: issues in methodology. https://www.ncbi.nlm.nih.gov/pubmed/?term=8164085 Compliance with medication and medical appointments is presumed to have a critical influence on outcomes of medical interventions. Readers of the medical literature should consider how compliance was measured and analyzed when interpreting the results of clinical trials. Table 4 suggests criteria for critical appraisal of compliance-related issues in reports of clinical trials. .   No .
Compliance compliance;controlled clinical trials; Journal Article Patient compliance and the conduct and interpretation of therapeutic trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3568692 Low patient compliance with prescribed treatments is a very common problem in clinical care and can seriously distort the generalizability and validity of controlled clinical trials.In all studies, because of the bias that noncompliance can have on results, the main analysis should include all those entered, whether or not compliant with the treatment regimen. .   No .
Compliance compliance Journal Article Patient compliance with drug treatment--new perspectives on an old problem. https://www.ncbi.nlm.nih.gov/pubmed/?term=1600344 Compared to other variables being considered in therapeutics, patient compliance has long been given minor attention although it affects every aspect of medical care. New perspectives related to the descriptive and explanatory side of the problem are outlined by giving examples from various therapeutic fields. .   No .
Compliance compliance Journal Article Patient compliance with therapeutic advice: a modern view. https://www.ncbi.nlm.nih.gov/pubmed/?term=2699001 Considering all the hurdles between the advice to take medication and the taking of it, that patients comply as well as they do is remarkable. Compliance with medical advice is far too complex, especially these days, to reduce to a simple yes-no dichotomy. A great deal of wisdom is called for, both on the patient's and on the physician's side, when medical advice is given. .   No .
Compliance compliance Journal Article Planning the size of a cohort study in the presence of both losses to follow-up and non-compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=7380984 Planning the size of a cohort study in the presence of both losses to follow-up and non-compliance. .   No .
Compliance compliance;clinical trials; interpretation Journal Article Role of patient compliance in clinical pharmacokinetics. A review of recent research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7988102 Until 1986 to 1987, the estimation of patient compliance with prescribed drug regimens in ambulatory care relied on methods that were biased either by their subjectivity or by the improvement in compliance that commonly occurs during the day or two prior to a scheduled examination, so called 'white-coat compliance'. In 1986 to 1987, 2 objective methods were developed: electronic monitoring and low-dose, slow-turnover chemical markers (digoxin or phenobarbital [phenobarbitone]) incorporated into dosage forms. While neither method is without limitations, both have enabled major advances in the understanding of patients' compliance with dosage regimens and, thus, the spectrum of drug exposure in ambulatory care. The new methods have also triggered not only a revival of interest in patient compliance and its determinants, but also new statistical approaches to interpreting the clinical correlates of widely variable drug administration, and thus drug exposure, in drug trials. The marker methods prove dose ingestion during the 3 to 7 days prior to blood sampling, but do not reveal the timing of doses. The electronic monitoring methods, i.e. time and date-stamping microcircuitry incorporated into drug packages, provide a continuous record of timing of presumptive doses throughout periods of many months, but do not prove dose ingestion. The electronic record has been judged robust enough to detect certain types of investigator fraud, and to support modelling projections of the complete time course of the plasma drug concentration during a trial. Both marker and electronic methods show that the predominant errors are those of omission, i.e. delays or omissions of scheduled doses. Patient interviews, diaries, and counts of returned, untaken doses have been shown by both marker and electronic monitoring methods to consistently and substantially to overestimate compliance. Monitoring of plasma drug concentrations also overestimates compliance, because white-coat compliance is prevalent, and the pharmacokinetic turnover of most drugs is rapid enough that measured concentrations of drug in plasma reflect only drug administration during the period of white-coat compliance. Thus, compliance is a great deal poorer in clinical trials than has been revealed by the older methods. The long-standing underestimation of poor compliance in drug trials has many implications for the interpretation of drug trials, for optimal dose estimation, for the interpretation of failed drug therapy, and for accurate labelling of prescription drugs. .   No .
Compliance compliance Journal Article Saturated fats, cholesterol, and dietary compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=1599344 Lack of response to a cholesterol-lowering diet can be caused by physiological nonresponsiveness, inadequate knowledge, or inability to change dietary habits (poor compliance). The purpose of this study was to evaluate the dietary compliance of hyperlipidemic individuals who received intensive initial dietary education and followup, and who showed an initial reduction of their plasma cholesterol levels.The results suggest the long-term compliance to the reduction of dietary saturated fat remains a problem, even in individuals who receive intensive initial training and show an early favorable response. Follow-up evaluation of hyperlipidemic patients who are receiving dietary therapy should take into account this behavioral pattern. It remains to be determined whether continuing supervision and better nutritional labeling will facilitate dietary compliance. .   No .
Compliance compliance Journal Article Should we pay the patient? Review of financial incentives to enhance patient compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=9314754 To determine whether financial incentives increase patients' compliance with healthcare treatments. Financial incentives can improve patient compliance. .   No .
Compliance compliance;sample size;clinical trials Journal Article The effect of poor compliance and treatment side effects on sample size requirements in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7951277%5Buid%5D Treatment side effects and associated noncompliance have methodological implications vital to the testing of new drugs. In this paper, we quantify the impact of these factors on sample size requirements in clinical trials. In the Lipid Research Clinics Trial, side effects caused treatment group compliance (50.8%) to be lower than placebo compliance (67.3%). Cholesterol reduction among treatment noncompliers was 35.2% of the reduction among compliers. Had treatment group compliance been as high as placebo compliance, 41% fewer patients would have been required to achieve the same statistical power and an expected 31% more coronary events would have been prevented. We conclude: Because they discourage patient compliance, treatment side effects can (1) cause large sample size increases, (2) lead to underestimates of true efficacy, and (3) contribute to potentially invalid negative conclusions in clinical trials. The impact of side effects goes well beyond the complications and patient discomforts with which they are associated. .   No .
Compliance power;sample size;compliance Journal Article Estimating the power of compliance-improving methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=11146148 Power and sample size: compliance .   No .
Confidence confidence Journal Article Confidence intervals in Clinical Chemistry. https://www.ncbi.nlm.nih.gov/pubmed/?term=8287529 Confidence intervals in Clinical Chemistry. .   No .
Confidence Intervals clinical trials;confidence intervals; Journal Article Confidence intervals for reporting results of clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3740683 Tests of statistical significance are often used by investigators in reporting the results of clinical research. Although such tests are useful tools, the significance levels are not appropriate indices of the size or importance of differences in outcome between treatments. Lack of "statistical significance" can be misinterpreted in small studies as evidence that no important difference exists. Confidence intervals are important but underused supplements to tests of significance for reporting the results of clinical investigations. Their usefulness is discussed here, and formulas are presented for calculating confidence intervals with types of data commonly found in clinical trials. .   No .
Confidence Intervals confidence intervals Journal Article Confidence intervals for research findings. https://www.ncbi.nlm.nih.gov/pubmed/?term=1554681 Confidence intervals for research findings. .   No .
Confidence Intervals clinical trials;confidence intervals Journal Article Inadequate size of 'negative' clinical trials in dermatology. https://www.ncbi.nlm.nih.gov/pubmed/?term=8471517 Use of confidence intervals to summarize clinical trial findings, so that readers can quickly decide whether clinically important treatment effects are plausible. .   No .
Confidence Intervals p values;confidence intervals Journal Article On P values and confidence intervals (why can't we P with more confidence?) https://www.ncbi.nlm.nih.gov/pubmed/?term=8504558 On P values and confidence intervals .   No .
Confidence Intervals clinical trials;analysis;repeated measures; confidence intervals Journal Article Statistical problems in the reporting of clinical trials. A survey of three medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=3614286 Reports of clinical trials often contain a wealth of data comparing treatments. This can lead to problems in interpretation, particularly when significance testing is used extensively. We examined 45 reports of comparative trials published in the British Medical Journal, the Lancet, or the New England Journal of Medicine to illustrate these statistical problems. The issues we considered included the analysis of multiple end points, the analysis of repeated measurements over time, subgroup analyses, trials of multiple treatments, and the overall number of significance tests in a trial report. Interpretation of large amounts of data is complicated by the common failure to specify in advance the intended size of a trial or statistical stopping rules for interim analyses. In addition, summaries or abstracts of trials tend to emphasize the more statistically significant end points. Overall, the reporting of clinical trials appears to be biased toward an exaggeration of treatment differences. Trials should have a clearer predefined policy for data analysis and reporting. In particular, a limited number of primary treatment comparisons should be specified in advance. The overuse of arbitrary significance levels (for example, P less than 0.05) is detrimental to good scientific reporting, and more emphasis should be given to the magnitude of treatment differences and to estimation methods such as confidence intervals. .   No .
Confidence Intervals confidence intervals Journal Article The case for confidence intervals. https://www.ncbi.nlm.nih.gov/pubmed/?term=1407930 The case for confidence intervals .   No .
Confidence Intervals clinical trials;confidence intervals;sample size Journal Article The case for confidence intervals in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8001360 A statistical wit once remarked that researchers often pose the wrong question and then proceed to answer that question incorrectly. The question that researchers intend to ask is whether or not a treatment effect is clinically significant. The question that is typically asked, however, is whether or not the treatment effect is statistically significant--a question that may be only marginally related to the issue of clinical impact. Similarly, the response, in the form of a p value, is typically assumed to reflect clinical significance but in fact reflects statistical significance. In an attempt to address this problem the medical literature over the past decade has been moving away from tests of significance and toward the use of confidence intervals. Concretely, study reports are moving away from "the difference was significant with a p value under 0.01" and toward "the one-year survival rate was increased by 20 percentage points with a 95% confidence interval of 15 to 24 percentage points." By focusing on what the effect is rather than on what the effect is not confidence intervals offer an appropriate framework for reporting the results of clinical trials. This paper offers a non-technical introduction to confidence intervals, shows how the confidence intervals framework offers advantages over hypothesis testing, and highlights some of the controversy that has developed around the application of this method. Additionally, we make the argument that studies which will be reported in terms of confidence intervals should similarly be planned with reference to confidence intervals. The sample size should be set to ensure that the estimates of effect size will be reported not only with adequate power but also with appropriate precision. .   No .
Confidence Limits confidence limits;power calculations Journal Article Confidence limits vs power calculations. https://www.ncbi.nlm.nih.gov/pubmed/?term=8173005 Confidence limits vs power calculations. .   No .
Confounding By Indication Propensity scores; confounding by indication Journal Article J Haukoos and R Lewis. The Propensity Score JAMA 2015 https://jamanetwork.com/journals/jama/fullarticle/2463242 A non-mathematical introduction to propensity scores 1 Any No 1
Confounding Factors (Epidemiology) Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Consulting consulting; Journal Article Estimating the value of an internal biostatistical consulting service. https://www.ncbi.nlm.nih.gov/pubmed/?term=10931516 Biostatistical consulting is a service business. Although a consulting biostatistician's goal is long-term collaborative relationships with investigators, this is the same as the long-term goal of any business: having a group of contented, satisfied customers. In this era of constrained resources, we must be able to demonstrate that the benefit a biostatistical consulting group provides to its organization exceeds its actual cost to the institution. In this paper, I provide both a theoretical framework for assessing the value of a biostatistical service and provide an ad hoc method to value the contribution of a biostatistical service to a grant. Using the methods described, our biostatistics group returns more than $6 for each dollar spent on institutional support in 1998. .   No .
Contengency Tables power;sample size;contengency tables Journal Article Power of testing proportions in small two-sample studies when sample sizes are equal. https://www.ncbi.nlm.nih.gov/pubmed/?term=8516594 Power/Sample size: Contingency Tables .   No .
Contingency Table contingency table; measure of association; chi square; proportions Journal Article Statistics review 8: Qualitative data - tests of association https://www.ncbi.nlm.nih.gov/pubmed/14975045 This review introduces methods for investigating relationships between two qualitative (categorical) variables. The chi square test of association is described, together with the modifications needed for small samples. The test for trend, in which at least one of the variables is ordinal, is also outlined. Risk measurement is discussed. The calculation of confidence intervals for proportions and differences between proportions are described. Situations in which samples are matched are considered. 2   No .
Contingency Tables power;sample size;contingency tables Journal Article Power evaluation of small drug and vaccine experiments with binary outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9463854 Power/Sample size: Contingency Tables .   No .
Contingency Tables power;sample size;contingency tables Journal Article Sample size determination based on Fisher's Exact Test for use in 2 x 2 comparative trials with low event rates. https://www.ncbi.nlm.nih.gov/pubmed/?term=1316828 Power/sample Size: contingency tables .   No .
Contradictions reading medical literature;contradictions Journal Article Contradictions in Highly cited Clinical Research https://www.ncbi.nlm.nih.gov/pubmed/16333000 Reading medical literature .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Dynamic balanced randomization for clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8134737 Clinical Trials: Randomization Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Empirical Evidence of Bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7823387 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Ensuring Balanced Distribution of Prognostic Factors in Treatment Outcome Research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7723001 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Generation of allocation sequences in randomized trials: chance, not choice. https://www.ncbi.nlm.nih.gov/pubmed/?term=11853818 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article How study design affects outcomes in comparisons of therapy. II: Surgical. https://www.ncbi.nlm.nih.gov/pubmed/?term=2727469 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article How to study design affects outcomes in comparisons of therapy. I: Medical https://www.ncbi.nlm.nih.gov/pubmed/?term=2727468 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Randomized versus Historical Controls for Clnical Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7058834 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Reporting Randomized Controlled Trials. An Experiment and a call for responses From Readers. https://www.ncbi.nlm.nih.gov/pubmed/?term=7897791 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Subverting Randomization in Controlled Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7474192 Clinical Trials: Randomization/Control Groups .   No .
Control Groups Clinical Trials;Randomization;Control Groups ;Adaptive Sampling Journal Article The randomization and stratification of patients to clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=4612056 Clinical Trials: Randomization/Control Groups & Adaptive Sampling .   No .
Control Groups Clinical Trials;Randomization;Control Groups Journal Article Treatment allocation Methods in clinical trials: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=3895341 Clinical Trials: Randomization/Control Groups .   No .
Controlled Clinical Trials controlled clinical trials;bias;randomization;bias Journal Article Bias in treatment assignment in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=6633598 Controlled clinical trials of the treatment of acute myocardial infarction offer a unique opportunity for the study of the potential influence on outcome of bias in treatment assignment. A group of 145 papers was divided into those in which the randomization process was blinded (57 papers), those in which it may have been unblinded (45 papers), and those in which the controls were selected by a nonrandom process (43 papers). At least one prognostic variable was maldistributed (P less than 0.05) in 14.0 per cent of the blinded-randomization studies, in 26.7 per cent of the unblinded-randomization studies, and in 58.1 per cent of the nonrandomized studies. Differences in case-fatality rates between treatment and control groups (P less than 0.05) were found in 8.8 per cent of the blinded-randomization studies, 24.4 per cent of the unblinded-randomization studies, and 58.1 per cent of the nonrandomized studies. These data emphasize the importance of keeping those who recruit patients for clinical trials from suspecting which treatment will be assigned to the patient under consideration. .   No .
Controlled Clinical Trials compliance;controlled clinical trials; Journal Article Patient compliance and the conduct and interpretation of therapeutic trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3568692 Low patient compliance with prescribed treatments is a very common problem in clinical care and can seriously distort the generalizability and validity of controlled clinical trials.In all studies, because of the bias that noncompliance can have on results, the main analysis should include all those entered, whether or not compliant with the treatment regimen. .   No .
Controls active;controls;bioequivalence Journal Article Evaluation of active control trials in AIDS. https://www.ncbi.nlm.nih.gov/pubmed/?term=2231306 Active Controls/Bioequivalence .   No .
Controls active;controls;bioequivalence Journal Article Treatment evaluation in active control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=3119201 Active Controls/Bioequivalence .   No .
Controversy clinical trials;controversy Journal Article Controversy in counting and attributing events in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=514321 Controversy in counting and attributing events in clinical trials. .   No .
Cores biostatistics;cores Journal Article Guidelines for budgeting biostatistics involvement in research projects. https://www.ncbi.nlm.nih.gov/pubmed/?term=8910957 Biostatistics Cores .   No .
Cores biostatistical;cores Journal Article The biostatistician in medical research: allocating time and effort. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481203 Biostatistical Cores .   No .
Correlated Binary Data Clinical trials; GEE; logistic regression; correlated binary data Journal Article Sample size and power calculations with correlated binary data https://www.ncbi.nlm.nih.gov/pubmed/11384786 sample size formula for correlated binary data Control Clin Trials. 2001 Jun;22(3):211-27. 5 Any No 4
Correlates correlates; Journal Article Correlates of nonadherence to hypertension treatment in an inner-city minority population. https://www.ncbi.nlm.nih.gov/pubmed/?term=1456334 OBJECTIVE: Adherence to treatment is a key factor in achieving blood pressure control among hypertensives. We examined correlates of nonadherence to hypertension treatment in an inner-city minority population. CONCLUSIONS: Changing the locus of care for hypertension from emergency rooms to primary care physicians may improve adherence to hypertension treatment in minority populations. .   No .
Correlation biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Correlation linear regression; correlation Journal Article Statistics review 7: Correlation and regression https://www.ncbi.nlm.nih.gov/pubmed/14624685 The present review introduces methods of analyzing the relationship between two quantitative variables. The calculation and interpretation of the sample product moment correlation coefficient and the linear regression equation are discussed and illustrated. Common misuses of the techniques are considered. Tests and confidence intervals for the population parameters are described, and failures of the underlying assumptions are highlighted. 2   No 2
Cost Assessment Cost Assessment Journal Article Problems in interpreting cost effectiveness in clinical trials. Experimental versus implementation costs. https://www.ncbi.nlm.nih.gov/pubmed/8306003 Cost Assessment .   No .
Cost Assessment Cost assessment Journal Article Screening mammography beginning at age 40 years: a reappraisal of cost-effectiveness. https://www.ncbi.nlm.nih.gov/pubmed/9610704 Cost Assessments .   No .
Cost Assessment Cost Assessment Journal Article Screening mammography and public health policy: the need for perspective. https://www.ncbi.nlm.nih.gov/pubmed/7603143 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article Pharmacoeconomics of hypertension control: basic principles of economic evaluation. https://www.ncbi.nlm.nih.gov/pubmed/?term=8868039 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article Economic evaluation of cholesterol-related interventions in general practice. An appraisal of the evidence. https://www.ncbi.nlm.nih.gov/pubmed/?term=10320860 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article Cost-effectiveness of nicotine gum as an adjunct to physician's advice against cigarette smoking. https://www.ncbi.nlm.nih.gov/pubmed/?term=3091857 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article Impact and cost-effectiveness of smoking interventions. https://www.ncbi.nlm.nih.gov/pubmed/?term=1497003 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article Cost benefit of treating hypertension. https://www.ncbi.nlm.nih.gov/pubmed/10423119 Cost Assessment .   No .
Cost Assessment Cost Assessment Journal Article How should cost data in pragmatic randomised trials be analysed? https://www.ncbi.nlm.nih.gov/pubmed/?term=10784550 Cost Assessment .   No .
Cost Assessments Cost Assessments Journal Article Comparison of analytic models for estimating the effect of clinical factors on the cost of coronary artery bypass graft surgery. https://www.ncbi.nlm.nih.gov/pubmed/?term=8455051 Cost Assessments .   No .
Cost Assessments Cost assessments Journal Article The clinical-economic trial: promise, problems, and challenges. https://www.ncbi.nlm.nih.gov/pubmed/?term=8720016 Cost assessments .   No .
Cost Assessments Cost assessments Journal Article Cost-effectiveness analysis of helicopter EMS for trauma patients. https://www.ncbi.nlm.nih.gov/pubmed/9326865 Cost assessments .   No .
Cost Assessments Cost Assessments Journal Article The economics of dying. The illusion of cost savings at the end of life. https://www.ncbi.nlm.nih.gov/pubmed/8302321 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article Cost-effectiveness analysis in heart disease, Part II: Preventive therapies. https://www.ncbi.nlm.nih.gov/pubmed/7831469 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article Economics of coronary artery bypass grafting. https://www.ncbi.nlm.nih.gov/pubmed/3160430 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article Screening mammography and public health policy: the need for perspective. https://www.ncbi.nlm.nih.gov/pubmed/7603143 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article Cost-effectiveness of HMG-CoA reductase inhibition for primary and secondary prevention of coronary heart disease. https://www.ncbi.nlm.nih.gov/pubmed/1899896 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article Cost-effectiveness of antihyperlipemic therapy in the prevention of coronary heart disease. The case of cholestyramine. https://www.ncbi.nlm.nih.gov/pubmed/3118060 Cost Assessments .   No .
Cost Assessments Cost Assessments Journal Article The cost-effectiveness of counseling smokers to quit. https://www.ncbi.nlm.nih.gov/pubmed/?term=2491762 Cost Assessments .   No .
Cost Effectiveness Cost effectiveness Journal Article Cost-effectiveness of regulations against using a cellular telephone while driving. https://www.ncbi.nlm.nih.gov/pubmed/?term=9917014 Cost effectiveness .   No .
Cost-Effectiveness Analysis observational data; propensity score methods; cost-effectiveness analysis Book Analysis of Observational Health Care Data Using SAS https://www.amazon.com/Analysis-Observational-Health-Care-Using/dp/1607642271/ref=sr_1_1?s=books&ie=UTF8&qid=1515099599&sr=1-1&keywords=analysis+of+observational+health+care+data+using+sas analysis of Observational Health Care Data Using SAS with many SAS code examples, includes examples of propensity score methods and cost-effectiveness analysis, etc. very practical and useful. 4 SAS Yes 4
Count Models regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Counterfactual Analysis causal mediation; mediator; observational data; counterfactual analysis; Website Causal Mediation Analysis with the CAUSALMED Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1991-2018.pdf Important policy and health care decisions often depend on understanding the direct and indirect (mediated) effects of a treatment on an outcome. For example, does a youth program directly reduce juvenile delinquent behavior, or does it indirectly reduce delinquent behavior by changing the moral and social values of teenagers? Or, for example, is a particular gene directly responsible for causing lung cancer, or does it have an indirect (mediated) effect through its influence on smoking behavior? Causal mediation analysis deals with the mechanisms of causal treatment effects, and it estimates direct and indirect effects. A treatment variable is assumed to have causal effects on an outcome variable through two pathways: a direct pathway and a mediated (indirect) pathway through a mediator variable. This paper introduces the CAUSALMED procedure, new in SAS/STAT® 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework. The paper also defines these causal mediation and related effects in terms of counterfactual outcomes and describes the assumptions that are required for unbiased estimation. Examples illustrate the ideas behind causal mediation analysis and the applications of the CAUSALMED procedure. 4 SAS No 3
Covariate Adjustment Covariate Adjustment Journal Article Practices and impact of primary outcome adjustment in randomized controlled trials: meta-epidemiologic study. https://www.ncbi.nlm.nih.gov/pubmed/?term=23851720 Covariate Adjustment .   No .
Covariate Adjustment Covariate Adjustment Journal Article The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=24755011 Covariate Adjustment .   No .
Cox Model survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Cox Proportional Hazards Models power analysis; bias (epidemiology); model adequacy; type I error; cox proportional hazards models; logistic regression. Journal Article Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression https://academic.oup.com/aje/article/165/6/710/63906 Commentary and simulation about back-of-the-envelope power analyses for logistic and Cox regression. 2 Any No 1
Cox Regression survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Cox Regression Models Cox Regression Models Journal Article The use of survival analysis techniques in evaluating the effect of long-term tacrine (Cognex) treatment on nursing home placement and mortality in patients with Alzheimer's disease. https://www.ncbi.nlm.nih.gov/pubmed/?term=8969976 Cox Regression Models .   No .
Criteria authorship; criteria Journal Article Criteria for authorship for statisticians in medical papers. https://www.ncbi.nlm.nih.gov/pubmed/9819828 Publication committees .   No .
Cross-Over Trials, Multi-Center Trials mixed models; repeated measures data; cross-over trials, multi-center trials Book applied mixed models in medicine https://www.amazon.com/Applied-Models-Medicine-Statistics-Practice/dp/1118778251 very useful reference for analysis of longitudinal and correlated data using mixed models. good examples, SAS codes provided 4   No 4
Cross-Validation resampling; bootstrap; jackknife; cross-validation; simulation Website Don't Be Loopy: Re-Sampling and Simulation the SAS Way http://www2.sas.com/proceedings/forum2007/183-2007.pdf An excellent paper by David Cassell presented the SAS user group about how to program resampling statistics in SAS. The most common way that people do simulations and re-sampling plans in SAS® is, in fact, the slow and awkward way. People tend to think in terms of a huge macro loop wrapped around a piece of SAS code, with additional chunks of code to get the outputs of interest and then to weld together the pieces from each iteration. But SAS is designed to work with by-processing, so there is a better way. A faster way. This paper will show a simpler way to perform bootstrapping, jackknifing, cross-validation, and simulations from established populations. It is simpler and more efficient to get SAS to build all the iterations in one long SAS data set, then use by-processing to do all the computations at once. This lets us use SAS features to gather automatically the information from all the iterations, for simpler computations afterward. 4 SAS No 3
Crossover Trials Crossover trials Journal Article Construction of uniform-balanced cross-over designs for any odd number of treatments. https://www.ncbi.nlm.nih.gov/pubmed/?term=10070673 Crossover trials .   No .
Cutpoints Cutpoints Journal Article Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. https://www.ncbi.nlm.nih.gov/pubmed/8182763 Cutpoints .   No .
Cutpoints Cutpoints Journal Article Selection of dichotomy limits for multifactorial prediction of arrhythmic events and mortality in survivors of acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=9458420 Cutpoints .   No .
Data publication bias;meta-analysis;data Journal Article Should unpublished data be included in meta-analyses? Current convictions and controversies https://www.ncbi.nlm.nih.gov/pubmed/8492400 Publication Bias .   No .
Data multicenter trials;analysis;data Journal Article Guidelines for quality assurance in multicenter trials: a position paper. https://www.ncbi.nlm.nih.gov/pubmed/?term=9741868 Multicenter trials: Analysis of Data .   No .
Data multicenter trials;analysis;data Journal Article Publications from multicentre clinical trials: statistical techniques and accessibility to the reader. https://www.ncbi.nlm.nih.gov/pubmed/?term=7701142 Multicenter Trials: analysis of Data .   No .
Data multicenter trials;analysis;data Journal Article Evaluation of multicentre clinical trial data using adaptations of the Mosteller-Tukey procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341865 Multicenter Trials: Analysis of Data .   No .
Data multicenter trials;analysis;data Journal Article Tests for qualitative treatment-by-centre interaction using a 'pushback' procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341864 Multicenter trials: Analysis of Data .   No .
Data ordered;alternatives;data;analysis Journal Article Analyzing data from ordered categories. https://www.ncbi.nlm.nih.gov/pubmed/6749191 Ordered Alternatives .   No .
Data ordered;alternatives;data;analysis Journal Article Standards for the use of ordinal scales in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3080061 Ordered Alternatives .   No .
Data ordered;alternatives;data;analysis Journal Article Ordinal scale and statistics in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3081161 Ordered Alternatives .   No .
Data multiple;comparisons;data;analysis Journal Article Gatekeeping Strategies for Avoiding False-Positive Results in Clinical Trials With Many Comparisons. https://www.ncbi.nlm.nih.gov/pubmed/?term=29049572 Multiple Comparisons .   No .
Data multiple;comparisons;data;analysis Journal Article P-value interpretation and alpha allocation in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/9708870 Multiple Comparisons .   No .
Data multiple comparisons;data;analysis Journal Article Invited commentary: Re: "Multiple comparisons and related issues in the interpretation of epidemiologic data". https://www.ncbi.nlm.nih.gov/pubmed/?term=9583708 Multiple Comparisons .   No .
Data multiple;comparisons;data;analysis Journal Article Multiple comparisons and related issues in the interpretation of epidemiologic data https://www.ncbi.nlm.nih.gov/pubmed/?term=7572970 Multiple Comparisons .   No .
Data multiple;comparisons;data;analysis Journal Article Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8629727 Multiple Comparisons .   No .
Data multiple;comparisons;data;analysis Journal Article Impact of multiple comparisons in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3661589 Multiple Comparisons .   No .
Data multiple;comparisons;data;analysis Journal Article Multiple hypothesis tests in multiple investigations. https://www.ncbi.nlm.nih.gov/pubmed/?term=7792449 Multiple Comparisons .   No .
Data multilevel;hierarchical;regress;data Journal Article Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures. https://www.ncbi.nlm.nih.gov/pubmed/?term=20949128 Multilevel/Hierarchical Regress .   No .
Data multilevel;hierarchical;regress;data Journal Article Tutorial in biostatistics. An introduction to hierarchical linear modelling. https://www.ncbi.nlm.nih.gov/pubmed/?term=10327531 Multilevel / Hierarchical Regress .   No .
Data multilevel;hierarchical;regress;data Journal Article Multi-level analysis in epidemiologic research on health behaviors and outcomes. https://www.ncbi.nlm.nih.gov/pubmed/1632420 Multilevel / Hierarchical Regress .   No .
Data group;sequential;test;survival analysis;data Other Stochastically Curtailed tests in Long-term Clinical Trials from Long Term Clinical Trials https://www.tandfonline.com/doi/abs/10.1080/07474948208836014 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article Statistics: the problem of examining accumulating data more than once. https://www.ncbi.nlm.nih.gov/pubmed/?term=4589874 Group Sequential Tests .   No .
Data group;sequential;tests;data;clinical trials Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Test .   No .
Data group;sequential;tests;design;analysis;data Journal Article Group sequential methods in the design and analysis of clinical trials https://www.jstor.org/stable/2335684?seq=1#metadata_info_tab_contents Group Sequential Tests .   No .
Data group;sequential;tests;clinical trials;data Journal Article Group sequential testing in clinical trials with multivariate observations: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8122047 Group Sequential Tests .   No .
Data group;sequential;test;data Journal Article Monitoring Clinical Trial Data for Evidence of Adverse or Beneficial Treatment Effects. P.L. Canner   Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article Discrete sequential boundaries for clinical trials https://academic.oup.com/biomet/article/70/3/659/247777 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article On choosing the number of interim analyses in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7187080 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article One-sample multiple testing procedure for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7082756 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article Can early stopping procedures impact significantly on the efficiency of clinical trials without serious loss of information? https://www.ncbi.nlm.nih.gov/pubmed/?term=6528138 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article Symmetric group sequential test designs. https://www.ncbi.nlm.nih.gov/pubmed/?term=2675998 Group Sequential Tests .   No .
Data group;sequential;tests;data Journal Article Monitoring treatment differences in long-term clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=588655 Group Sequential Tests .   No .
Data phase II trials;data Journal Article Comparison of error rates in single-arm versus randomized phase II cancer clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=20212253 Phase II Trials .   No .
Data phase II trials;data Journal Article Randomized phase II trials: a long-term investment with promising returns. https://www.ncbi.nlm.nih.gov/pubmed/?term=21709274 Phase II Trials .   No .
Data phase II trials;randomization;data Journal Article Randomized phase II trials: what does randomization gain? https://www.ncbi.nlm.nih.gov/pubmed/15699476 Phase II Trials .   No .
Data phase II trials;data Journal Article Planned versus attained design in phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1604065 Phase II Trials .   No .
Data phase II trials;data;design Journal Article Planned versus attained design in phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1604065 Phase II Trials .   No .
Data phase II trials;design;data Journal Article Optimal two-stage designs for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702835 Phase II Trials .   No .
Data power;sample size;ordered;categorical;data Journal Article Sample size calculations for ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/8134732 Power/sample size: ordered categorical data .   No .
Data Analysis Data Interpretation; Data analysis; python Online Interactive Course Data Analysis and Interpretation Specialization https://www.coursera.org/specializations/data-analysis Per the course website, this 5-course specialiation is prepared by the Wesleyan University. It helps you learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. . SPSS No .
Data Analysis statistical methods;data analysis;reliability Journal Article Biostatistics: how to detect, correct and prevent errors in the medical literature. https://www.ncbi.nlm.nih.gov/pubmed/?term=7349923 Approximately half the articles published in medical journals that use statistical methods use them incorrectly. These errors are so widespread that the present system of peer review has not been able to control them. This article presents a few rules of thumb to help readers identify questionable statistical analysis and estimate what the authors would have concluded had they used appropriate statistical methods. To prevent such errors from appearing, journals should secure review by someone knowledgeable in statistics before accepting a manuscript. In addition, human research committees should insist that an investigator define an appropriate strategy for data analysis before approving a protocol. Such policies should quickly and effectively increase the reliability of the clinical and scientific literature. .   No .
Data Analysis data analysis Journal Article Preliminary report: Findings from the aspirin component of the ongoing Physicians' HealthStudy. https://www.ncbi.nlm.nih.gov/pubmed/3275899 data analysis .   No .
Data Analysis data analysis;paired data survival methods Journal Article A comparison of several tests for censored paired data. https://www.ncbi.nlm.nih.gov/pubmed/1579758 Data Analysis: paired data-survival methods .   No .
Data Analysis data analysis;paired data survival methods Journal Article Modelling paired survival data with covariates. https://www.ncbi.nlm.nih.gov/pubmed/?term=2655727 Data Analysis: Paired data-survival methods .   No .
Data Analysis data analysis;paired data Journal Article Methods to quantify the relation between disease progression in paired eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=10853635 Data Analysis:Paired data .   No .
Data Analysis data analysis;paired data Journal Article Statistical methodology for paired cluster designs. https://www.ncbi.nlm.nih.gov/pubmed/3661544 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Accounting for the correlation between fellow eyes in regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/1543458 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Comparison of alternative regression models for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/?term=8073198 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Regression analysis for correlated data. https://www.ncbi.nlm.nih.gov/pubmed/8323597 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Maximum likelihood regression methods for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/2281239 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Maximum likelihood regression methods for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/2281239 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Analyzing correlated binary data using SAS. https://www.ncbi.nlm.nih.gov/pubmed/2350962 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Statistical methods in ophthalmology: an adjustment for the intraclass correlation between eyes. https://www.ncbi.nlm.nih.gov/pubmed/7082754 Data Analysis;Paired data .   No .
Data Analysis data analysis;paired data Journal Article Multivariate methods in ophthalmology with application to other paired-data situations. https://www.ncbi.nlm.nih.gov/pubmed/?term=6534406 Data Analysis:Paired data .   No .
Data Analysis data analysis;paired data Journal Article Two eyes or one? The data analyst's dilemma. https://www.ncbi.nlm.nih.gov/pubmed/3173980 Data Analysis;Paired data .   No .
Data Analysis data analysis;paired data Journal Article Eyes or patients? Traps for the unwary in the statistical analysis of ophthalmological studies. https://www.ncbi.nlm.nih.gov/pubmed/3663557 Data Analysis: Paired data .   No .
Data Analysis data analysis;paired data Journal Article Marginal models for correlated binary responses with multiple classes and multiple levels of nesting. https://www.ncbi.nlm.nih.gov/pubmed/?term=1420848 Data Analysis; Paired data .   No .
Data Analysis data analysis;paired data Journal Article Statistical analysis of multi-eye data in ophthalmic research. https://www.ncbi.nlm.nih.gov/pubmed/?term=4019113 Data Analysis;Paired data .   No .
Data Analysis data analysis;paired data Journal Article Appropriate statistical methods to account for similarities in binary outcomes between fellow eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8163336 Data Analysis:Paired data .   No .
Data Analysis data analysis Journal Article A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6630407 data analysis: general issues .   No .
Data Analysis data analysis Journal Article Statistical data analysis in the computer age. https://www.ncbi.nlm.nih.gov/pubmed/?term=17746394 Data Analysis: General issues .   No .
Data Analysis data analysis Journal Article A strategy to use soft data effectively in randomized controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/2925965 Data analysis: general issues .   No .
Data Analysis Data Analysis Other A Bayesian Approach to Multicenter Trials and Metaanalysis   Data Analysis: General Issues .   No .
Data Analysis data analysis Journal Article Statistics in practice. Comparing the means of several groups. https://www.ncbi.nlm.nih.gov/pubmed/4058548 Data Analysis: General Issues .   No .
Data Analysis data analysis Journal Article The incorrect use of Chi-square analysis for paired data. https://www.ncbi.nlm.nih.gov/pubmed/?term=780018 Data analysis: General Issues .   No .
Data Analysis data analysis Journal Article Multifactorial analysis of family data ascertained through truncation: a comparative evaluation of two methods of statistical inference. https://www.ncbi.nlm.nih.gov/pubmed/?term=3348215 Data Analysis: General Issues .   No .
Data Analysis data analysis Journal Article The role of path analysis in coronary heart disease research. https://www.ncbi.nlm.nih.gov/pubmed/6610877 Data Analysis: General Issues .   No .
Data Analysis data analysis Journal Article Maximum utilization of the life table method in analyzing survival. https://www.ncbi.nlm.nih.gov/pubmed/?term=13598782 Data Analysis: General issues .   No .
Data Analysis data analysis Journal Article The analysis of the two-period repeated measurements crossover design with application to clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/843578 Data analysis: general issues .   No .
Data Analysis data analysis Journal Article Advantages of examining multicollinearities in regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=843577 Data Analysis: General Issues .   No .
Data Analysis data analysis Book Biostatistics in Pharmacology https://beckercat.wustl.edu/cgi-bin/koha/opac-detail.pl?biblionumber=49970 Data Analysis: General Issues .   No .
Data Analysis longitudinal;study design;data analysis;linear models;laird;ware Journal Article Families of lines: random effects in linear regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/3379003 Longitudinal Studies Design: data analysis: linear (Laird/Ware) Models .   No .
Data Analysis Longitudinal;Study Design;data analysis;linear Models;Laird;Ware Journal Article The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3526500 Longitudinal Studies Design;data analysis;linear (Laird/Ware) Models .   No .
Data Analysis Examples SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Data Interpretation Data Interpretation; Data analysis; python Online Interactive Course Data Analysis and Interpretation Specialization https://www.coursera.org/specializations/data-analysis Per the course website, this 5-course specialiation is prepared by the Wesleyan University. It helps you learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. . SPSS No .
Data Interpretation, Statistical Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Data Management data management Online Interactive Course Managing Data Analysis https://www.coursera.org/learn/managing-data-analysis Per the course website, this is course 3 of 5 in the Executive Data Science Specialization. It is prepared by JHU as a one-week course and describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. . Any Yes .
Data Management Data Management; sharing data Online Interactive Course Research Data Management and Sharing https://www.coursera.org/learn/data-management Per the course website, this course was prepared by the University of North Carolina at Chapel Hill and the University of Edinburgh to provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories. . Any Yes .
Data Management data management Journal Article Single Vs. Double Data Entry in CAST https://www.ncbi.nlm.nih.gov/pubmed/?term=1334820 Data management .   No .
Data Management data management Journal Article Computer-Assisted Data Collection in Multicenter Epidemiologic Research https://www.ncbi.nlm.nih.gov/pubmed/?term=2161309 data management .   No .
Data Management data management Journal Article Do Information Systems Improve the Quality of Clinical Research? Results of a Randomized Trial in a Cooperative Multi-institutional Cancer Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=7237997 Data Management .   No .
Data Merging Data merging Online Interactive Course Combining and Analyzing Complex Data https://www.coursera.org/learn/data-collection-analytics-project Per the course website, in this course, prepared by the University of Maryland, College Park, you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R . Any Yes .
Data Modeling Least Squares; Data modeling; Statistics Online Interactive Course Advanced Linear Models for Data Science 1: Least Squares https://www.coursera.org/learn/linear-models Per the course website, this course is prepared by JHU to help provide: A basic understanding of linear algebra and multivariate calculus; A basic understanding of statistics and regression models; At least a little familiarity with proof based mathematics; and Basic knowledge of the R programming language. . Any Yes .
Data Monitoring DSMB;data monitoring;guidelines Journal Article Data monitoring committees and interim monitoring guidelines. https://www.ncbi.nlm.nih.gov/pubmed/10503800 DSMB .   No .
Data Science data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
Data Science Data Science; SQL; importing and cleaning data; time series; machine learning; Online Interactive Course DataCamp - courses for Data Science https://www.datacamp.com/home This provides high quality courses on Data Science at both an introductory and advanced level using either R or Python. Some courses are free, others require a monthly fee to access. 1 R, Other Yes 2
Data Transformations biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Data Transformations distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Data Transformations data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
Data Visualization data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
Design design Online Interactive Course Designing, Running, and Analyzing Experiments https://www.coursera.org/learn/designexperiments Per the course website, this is course 7 of 8 in the Interaction Design Specialization prepared by UC San Diego. It will help learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs. . Any Yes .
Design randomized clinical trials; sample size; algorithm;design; Journal Article A comprehensive algorithm for determining whether a run-in strategy will be a cost-effective design modification in a randomized clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8446807 In randomized clinical trials, poor compliance and treatment intolerance lead to reduced between-group differences, increased sample size requirements, and increased cost. A run-in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run-in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step-by-step algorithm through which one can estimate the cost-effectiveness of a potential run-in. Because the cost-effectiveness of a run-in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run-in on the required number of randomized, eligible, and screened subjects. Run-in strategies are most likely to be cost-effective when: (1) per patient costs during the post-randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run-in is inexpensive; (5) for most patients, the run-in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run-in are treatment intolerant or non-compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost-effectiveness of run-in strategies are stringent. In particular, if the only purpose of a run-in is to exclude ordinary partial compliers, the run-in will frequently add to the cost of the trial. Often, the cost-effectiveness of a run-in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects. .   No .
Design randomization;design; Journal Article A method for assessing the quality of a randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=7261638 A system has been constructed to evaluate the design, implementation, and analysis of randomized control trials (RCT). The degree of quadruple blinding (the randomization process, the physicians and patients as to therapy, and the physicians as to ongoing results) is considered to be the most important aspect of any trial. The analytic techniques are scored with the same emphasis as is placed on the control of bias in the planning and implementation of the studies. Description of the patient and treatment materials and the measurement of various controls of quality have less weight. An index of quality of a RCT is proposed with its pros and cons. If published papers were to approximate these principles, there would be a marked improvement in the quality of randomized control trials. Finally, a reasonable standard design and conduct of trials will facilitate the interpretation of those with conflicting results and help in making valid combinations of undersized trials. .   No .
Design design; randomization;clinical trials Journal Article A new design for randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=431682 This paper proposes a new method for planning randomized clinical trials. This method is especially suited to comparison of a best standard or control treatment with an experimental treatment. Patients are allocated into two groups by a random or chance mechanism. Patients in the first group receive standard treatment; those in the second group are asked if they will accept the experimental therapy; if they decline, they receive the best standard treatment. In the analyses of results, all those in the second group, regardless of treatment, are compared with those in the first group. Any loss of statistical efficiency can be overcome by increased numbers. This experimental plan is indeed a randomized clinical trial and has the advantage that, before providing consent, a patient will know whether an experimental treatment is to be used. .   No .
Design design;interpretation;clinical research Journal Article Adherence to treatment and health outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8250647 Adherence (or compliance) is the extent to which a person's behavior coincides with medical or health advice. Recent evidence indicates that patients who adhere to treatment, even when that treatment is a placebo, have better health outcomes than poorly adherent patients. Based on this evidence, we now believe that the outcomes of treatment are not solely attributable to the specific action of a drug, but may also depend on other nonspecific therapeutic effects. We consider the implications of these findings for the design and interpretation of clinical research as well as for the care of patients. .   No .
Design clinical trial;randomization;design;variation Journal Article Complexity and contradiction in clinical trial research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3548349 Randomized clinical trials have become the accepted scientific standard for evaluating therapeutic efficacy. Contradictory results from multiple randomized clinical trials on the same topic have been attributed either to methodologic deficiencies in the design of one of the trials or to small sample sizes that did not provide assurance that a meaningful therapeutic difference would be detected. When 36 topics with conflicting results that included over 200 randomized clinical trials in cardiology and gastroenterology were reviewed, it was discovered that results of randomized clinical trials often disagree because the complexity of the randomized clinical trial design and the clinical setting creates inconsistencies and variation in the therapeutic evaluation. Nine methodologic sources of this variation were identified, including six items concerned with the design of the trials, and three items concerned with interpretation. The design issues include eligibility criteria and the selection of study groups, baseline differences in the available population, variability in indications for the principal and concomitant therapies, protocol requirements of the randomized clinical trial, and management of intermediate outcomes. The issues in interpreting the trials include the regulatory effects of treatments, the frailty of double-blinding, and the occurrence of unexpected trial outcomes. The results of this review suggest that pooled analyses of conflicting results of randomized clinical trials (meta-analyses) may be misleading by obscuring important distinctions among trials, and that enhanced flexibility in strategies for data analysis will be needed to ensure the clinical applicability of randomized clinical trial results. .   No .
Design compliance;randomization;design;interpretation;clinical trials Journal Article Compliance with an experimental drug regimen for treatment of asthma: its magnitude, importance, and correlates. https://www.ncbi.nlm.nih.gov/pubmed/?term=6389582 This paper reports on data from a double-blind, randomized controlled study of out-patient use of corticosteroids following an acute asthma attack. Issues related to compliance are examined, including: (1) the extent of non-compliance; (2) impact of non-compliance on interpreting the drug trial results; and (3) correlates of non-compliance. Of the 102 cases enrolled in the study, 25.5% were excluded from analysis because they were lost to follow-up (10.8%) or non-compliers (14.7%). Based on data for compliers, the drugs were found to reduce relapse rates and asthma symptomatology; when non-compliers were included in the analysis, the steroid drug appeared ineffective for reducing relapses and less effective for improving overall illness status. Examination of 24 potential correlates of compliance yielded a few significant associations, and only the "usual habit of compliance" correlation suggests an avenue for future action. The implications of the study findings for design and interpretation of clinical trials, as well as for improved management of chronic diseases, are discussed. .   No .
Design group-randomized trials; design; analysis Journal Article Design and analysis of group-randomized trials: a review of recent methodological developments. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998806 We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials. .   No .
Design design;analysis;group-randomized trials; methods Journal Article Design and analysis of group-randomized trials: a review of recent practices. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998802 We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published. .   No .
Design design; analysis;randomized clinical trials Journal Article Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. analysis and examples. https://www.ncbi.nlm.nih.gov/pubmed/?term=831755 Part II of report which describes efficient methods of analysis of randomized clinical trials in which we wish to compare the duration of survival (or the time until some other untoward event first occurs) among different groups of patients. .   No .
Design design;sample size;power analysis; Journal Article Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification. https://www.ncbi.nlm.nih.gov/pubmed/?term=3567285%5Buid%5D When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates. .   No .
Design methodology;bias;randomized trials;design Journal Article Methodological bias in cluster randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=15743523 Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials. Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants. .   No .
Design cluster randomized trials;design;sample size Journal Article Methods for sample size determination in cluster randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=26174515 The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. .   No .
Design longitudinal studies;design Journal Article Planning a longitudinal study. I. Sample size determination. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759580 Longitudinal Studies - Design .   No .
Design longitudinal studies;design Journal Article Planning a longitudinal study. II. Frequency of measurement and study duration. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759581 Longitudinal Studies-Design .   No .
Design group;sequential;tests;design;analysis;data Journal Article Group sequential methods in the design and analysis of clinical trials https://www.jstor.org/stable/2335684?seq=1#metadata_info_tab_contents Group Sequential Tests .   No .
Design phase II trials;data;design Journal Article Planned versus attained design in phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1604065 Phase II Trials .   No .
Design phase II trials;design;data Journal Article Optimal two-stage designs for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702835 Phase II Trials .   No .
Design paired;community;design;sample size Journal Article Aspects of statistical design for the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/?term=1315664 Paired Community Design Sample Size .   No .
Designs paired;community;designs;sample;size Journal Article Breaking the matches in a paired t-test for community interventions when the number of pairs is small. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481187 Paired Community Designs Sample Size .   No .
Designs paired;community;designs;sample size Journal Article Planning for the appropriate analysis in school-based drug-use prevention studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=2212183 Paired Community Designs Sample Size .   No .
Designs paired;community;designs;sample;size Journal Article Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=2066748 Paired Community Designs Sample Size .   No .
Designs paired;community;designs;sample size Journal Article A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. https://www.ncbi.nlm.nih.gov/pubmed/?term=2084005 Paired Community Design Sample Size .   No .
Diagnostic Medicine diagnostic medicine; ROC; AUC; sample size calculation for diagnostic test Book Statistical Methods in Diagnostic Medicine https://www.amazon.com/Statistical-Methods-Diagnostic-Medicine-Xiao-Hua/dp/0470183144/ref=sr_1_1?s=books&ie=UTF8&qid=1515100984&sr=1-1&keywords=statistical+methods+in+diagnostic+medicine basic concepts and methods in diagnostic medicine such as ROC and AUC, estimation and hypothesis testing, sample size calculation for sensitivity, specificity, ROC and AUC, regression analysis for independent and correlated ROC data 6 Any Yes 4
Diagnostic Test reading medical literature;guides;diagnostic test;patient care Journal Article Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8309035 Reading medical literature .   No .
Diagnostic Test reading medical literature;diagnostic test;study results Journal Article Users' Guides to the Medical Literature III. How to use an article about a diagnostic test A. Are the Results of the study valid? https://www.ncbi.nlm.nih.gov/pubmed/?term=8283589 Reading Medical Literature .   No .
Disseminiation disseminiation Journal Article Why current publication practices may distort science. https://www.ncbi.nlm.nih.gov/pubmed/?term=18844432 dissemination .   No .
Distribution Of Data distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Doctor Degree PhD; Public Health; Doctor degree Online Interactive Course Doctor of Public Health  https://www.jhsph.edu/academics/degree-programs/doctoral-programs/doctor-of-public-health/ Doctor of Public Health offered by JHU/ Bloomberg School of Public Health 4 Any, SAS Yes .
Dsmb DSMB;data monitoring;guidelines Journal Article Data monitoring committees and interim monitoring guidelines. https://www.ncbi.nlm.nih.gov/pubmed/10503800 DSMB .   No .
Editorial Decision publication bias;editorial decision Journal Article Publication bias in editorial decision making. https://www.ncbi.nlm.nih.gov/pubmed/?term=12038924 Publication Bias .   No .
Effect On Medical Practice Clinical Trials; Effect on Medical Practice Journal Article Bias in Analytic Research https://www.ncbi.nlm.nih.gov/pubmed/?term=447779 Clinical Trials: Effect on Medical Practice .   No .
Effect On Medical Practice Clinical Trials;Effect on Medical Practice Journal Article Evidence Favoring The Use of Anticoagulants in the Hospital Phase of Acute Myocardial Infarction https://www.ncbi.nlm.nih.gov/pubmed/?term=909566 Clinical Trials: Effect on Medical Practice .   No .
Effect Size effect size; power analysis; sample size Journal Article Measures of Clinical Significance https://www.ncbi.nlm.nih.gov/pubmed/14627890 This article outlines effect sizes for common situations in clinical research 2   No 1
Effect Sizes observational data; effect sizes; large data Journal Article Tips for Analyzing Large Data Sets From the JAMA Surgery Statistical Editors https://www.ncbi.nlm.nih.gov/pubmed/29617520 With the advent of administrative databases and patient registries, big data is increasingly accessible to researchers. The large sample size of these data sets make the study of rare outcomes easier and provide the potential to determine national estimates and regional variations. As such, the JAMA Surgery editors and reviewers have seen more submissions using big data to answer clinical and policy-related questions. However, no database is completely free of bias and measurement error. With bigger data, random signals may denote statistical significance, and precision may be incorrectly inferred because of narrow confidence intervals. While many principles apply to all studies, the importance of these methodological issues is amplified in large, complex data sets. 1 Any No 1
Effectiveness efficacy; effectiveness Journal Article A Primer on Effectiveness and Efficacy Trials https://www.ncbi.nlm.nih.gov/pubmed/24384867 Efficacy and Effectiveness .   No .
Efficacy efficacy; effectiveness Journal Article A Primer on Effectiveness and Efficacy Trials https://www.ncbi.nlm.nih.gov/pubmed/24384867 Efficacy and Effectiveness .   No .
Efficacy publication bias;trials;efficacy Journal Article Selective publication of antidepressant trials and its influence on apparent efficacy. https://www.ncbi.nlm.nih.gov/pubmed/?term=18199864 Publication bias .   No .
Enterprise Guide SAS tutorials; Enterprise Guide; SAS Studio;Viya Video Instruction Portal to 200 SAS Video Tutorials http://video.sas.com/#category/videos/how-to-tutorials Video tutorials on a wide variety of SAS topics, from programming to statistics. Many use SAS Studio or Enterprise Guide. 1 SAS No 1
Epidemilogy GEE; Generalized Estimating Equations; epidemilogy Journal Article Statistical analysis of correlated data using generalized estimating equations: an orientation https://www.ncbi.nlm.nih.gov/pubmed/12578807 The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data. 2 Any No 2
Epidemiology epidemiology Website Epidemiology for the uninitiated https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated Provide an introduction to epidemiology 1   No 1
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. https://www.ncbi.nlm.nih.gov/pubmed/16522836 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Lessons from and cautions about noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16522840 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Equivalence priority;equivalence;trials;bioequivalence Journal Article An approximate unconditional test of non-inferiority between two proportions. https://www.ncbi.nlm.nih.gov/pubmed/?term=10931513 Nonin Priority/ Equivalence Trials - Bioequivalence .   No .
Equivalence priority;equivalence;trials;bioequivalence Journal Article Equivalence Trials https://www.ncbi.nlm.nih.gov/pubmed/?term=9329939 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Significance testing to establish equivalence between treatments, with special reference to data in the form of 2X2 tables. https://www.ncbi.nlm.nih.gov/pubmed/?term=588654 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Bioequivalence revisited. https://www.ncbi.nlm.nih.gov/pubmed/?term=1485060 Nonin priority/Equivalence trials - Bioequivalence. .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article Conventional null hypothesis testing in active control equivalence studies. https://www.ncbi.nlm.nih.gov/pubmed/8582153 Nonin priority/Equivalence Trials - Bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials;bioequivalence Journal Article A comparison of continuous infusion of alteplase with double-bolus administration for acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/9340504 Nonin prioritiy/Equivalence Trials - bioequivalence .   No .
Equivalence nonin;priority;equivalence;trials Journal Article Good enough: a primer on the analysis and interpretation of noninferiority trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16818930 Nonin Priority/Equivalence Trials .   No .
Equivalence nonin;priority;equivalence;trials Journal Article Alternatives to Hazard Ratios for Comparing the Efficacy or Safety of Therapies in Noninferiority Studies. https://www.ncbi.nlm.nih.gov/pubmed/26054047 Nonin Priority/Equivalence Trials .   No .
Equivalence nonin;priority;equivalence;trials Journal Article Non-inferiority trials: design concepts and issues - the encounters of academic consultants in statistics. https://www.ncbi.nlm.nih.gov/pubmed/?term=12520555 Nonin Priority/Equivalence Trials .   No .
Equivalence nonin;priority;equivalence;trials Journal Article Implementation of an adaptive group sequential design in a bioequivalence study. https://www.ncbi.nlm.nih.gov/pubmed/?term=17436336 Nonin Priority/Equivalence Trials .   No .
Equivalence nonin;priority;equivalence;trials Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority/Equivalence Trials .   No .
Equivalence Trials Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Equivalence Trials equivalence trials Website Tests of Equivalence and Confidence Intervals for Effect Sizes http://core.ecu.edu/psyc/WuenschK/docs30/Equivalence-EffectSizeCI.pdf Equivalence trials .   No .
Estimability estimability Journal Article Estimability and estimation in case-referent studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=1251836 The concepts that case-referent studies provide for the estimation of "relative risk" only if the illness is "rare", and that the rates and risks themselves are inestimable, are overly superficial and restrictve. The ratio of incidence densities (forces of morbidity)-and thereby the instantaneous risk-ratio-is estimable without any rarity-assumption. Long-term risk-ratio can be computed through the coupling of case-referent data on exposure rates for various age-categories with estimates, possibly from the study itself, or the corresponding age-specific incidence-densities for the exposed and nonexposed combined-but again, no rarity-assumption is involved. Such data also provide for the assessment of exposure-specific absolute incidence-rates and risks. Point estimation of the various parameters can be based on simple relationships among them, and in interval estimation it is sufficient simply to couple the point estimate with the value of the chi square statistic used in significance testing. .   No .
Estimation Estimation; Inference; Statistics Online Interactive Course Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation https://www.coursera.org/learn/statistical-reasoning-1 Per the course website, this course, prepared by JHU, provides a conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics. . Any Yes .
Ethics Ethics Online Interactive Course Data Science Ethics https://www.coursera.org/learn/data-science-ethics Per the course website, this course, prepared by the University of Michigan, provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. . Any No .
Ethics clinical trials;ethics Journal Article Clinical trials--are they ethical? https://www.ncbi.nlm.nih.gov/pubmed/?term=1709257 Clinical trials--are they ethical? .   No .
Ethics cluster randomization trials;ethics Journal Article Pitfalls of and controversies in cluster randomization trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998805 It is now well known that standard statistical procedures become invalidated when applied to cluster randomized trials in which the unit of inference is the individual. A resulting consequence is that researchers conducting such trials are faced with a multitude of design choices, including selection of the primary unit of inference, the degree to which clusters should be matched or stratified by prognostic factors at baseline, and decisions related to cluster subsampling. Moreover, application of ethical principles developed for individually randomized trials may also require modification. We discuss several topics related to these issues, with emphasis on the choices that must be made in the planning stages of a trial and on some potential pitfalls to be avoided. .   No .
Ethics ethics;statistics Journal Article Statistics in medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=7187083 The general standard of statistics in medical journals is poor. This paper considers the reasons for this with illustrations of the types of error that are common. The consequences of incorrect statistics in published papers are discussed; these involve scientific and ethical issues. Suggestions are made about ways in which the standard of statistics may be improved. Particular emphasis is given to the necessity for medical journals to have proper statistical refereeing of submitted papers. .   No .
Ethics Clinical Trials;Ethics;Fraud;Monitoring Journal Article The dangers of inferring treatment effects from observational data: a case study in HIV infection https://www.ncbi.nlm.nih.gov/pubmed/?term=11943438 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Ethics Clinical Trials;Ethics;Fraud;Monitoring Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Ethics Clinical Trials;Ethics;Fraud;Monitoring Journal Article When Was a 'Negative' Clinical Trail Big Enough? How Many Patients You Needed Depends on What You Found. https://www.ncbi.nlm.nih.gov/pubmed/?term=3985731 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Evaluating Treatment Efficacy randomization;evaluating treatment efficacy; Journal Article Influence of adherence to treatment and response of cholesterol on mortality in the coronary drug project. https://www.ncbi.nlm.nih.gov/pubmed/?term=6999345 The Coronary Drug Project was carried out to evaluate the efficacy and safety of several lipid-influencing drugs in the long-term treatment of coronary heart disease. The five-year mortality in 1103 men treated with clofibrate was 20.0 per cent, as compared with 20.9 per cent in 2789 men given placebo (P = 0.55). Good adherers to clofibrate, i.e., patients who took 80 per cent of more of the protocol prescription during the five-year follow-up period, had a substantially lower five-year mortality than did poor adherers to clofibrate (15.0 vs. 24.6 per cent; P = 0.00011). However, similar findings were noted in the placebo group, i.e., 15.1 per cent mortality for good adherers and 28.3 per cent for poor adherers (P = 4.7x10-16). These findings and various other analyses of mortality in the clofibrate and placebo groups of the project show the serious difficulty, if not impossibility, of evaluating treatment efficacy in subgroups determined by patient responses (e.g., adherence or cholesterol change) to the treatment protocol after randomization. .   No .
Evaluation medical literature;evaluation;clinical research Journal Article Critical Evaluation of Clinical Research https://www.ncbi.nlm.nih.gov/pubmed/?term=7811181 Reading Medical Literature .   No .
Exact Confidence Intervals For A Proportion Exact confidence intervals for a proportion; bootstrap; competing risk; case-control matching; survival analysis Software to Download Locally Written SAS Macros http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros The SAS macros below were written and are maintained by Mayo Clinic staff. They contain SAS source code, a brief description of the macro's function and an example of the macro call. 4 SAS No 3
Excel Excel Online Interactive Course Mastering Data Analysis in Excel https://www.coursera.org/learn/analytics-excel Per the course website, this course focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. . Any Yes .
Excel Excel Online Interactive Course Introduction to Data Analysis Using Excel https://www.coursera.org/learn/excel-data-analysis Per the course website, this course prepared by Rice University, gives an introduction into using excel ni data analysis. Module 1 would be an introduction into spreadsheets; module 2 explains spreadsheets functions to organize data; module 3 is an introduction to filtering, pivot tables, and charts; and module 4 explians advanced graphing and charting . Any Yes .
Executive Data Executive Data Online Interactive Course Executive Data Science Specialization https://www.coursera.org/specializations/executive-data-science Per the course website, this is a 5-course specialiation prepared by JHU to provide the following: 1. The different roles in the data science team including data scientist and data engineer; 2. How the data science team relates to other teams in an organization; 3. What are the expected qualifications of different data science team members; 4. Relevant questions for interviewing data scientists; 5. How to manage the onboarding process for the team; 6. How to guide data science teams to success; 7. How to encourage and empower data science teams . Any Yes .
Factor Analysis network analysis; factor analysis Website Psych Networks http://psych-networks.com/ Rich website for the use of network analysis, particularly for analyses in psychology by one of the chief proponents of the technique. In many aspects, the analyses are similar to those of factor analysis, but with differing underlying assumptions. Longitudinal models are included. 4 R No 4
Factor Analysis factor analysis Journal Article Factor analysis: an introduction to essentials II. The role of factor analysis in research. https://www.ncbi.nlm.nih.gov/pubmed/?term=14338675 Factor analysis .   No .
Feinstein Papers feinstein papers Journal Article Coffee and Pancreatic Cancer https://www.ncbi.nlm.nih.gov/pubmed/?term=7253179 Feinstein papers and responses .   No .
Feinstein Papers feinstein papers Journal Article Scientific Standards in Epidemiologic Studies of the Menace of Daily Life https://www.ncbi.nlm.nih.gov/pubmed/3057627 Feinstein papers .   No .
Feinstein Papers feinstein papers Journal Article Current problems and future challenges in randomized clinical trials https://www.ncbi.nlm.nih.gov/pubmed/6488491 Feinstein papers and responses .   No .
File Search file search; string search Software to Download Agent Ransack string-search tool https://www.mythicsoft.com/agentransack/ Agent Ransack is a very powerful tool for searching for files and strings within files. Runs on Windows. I use it multiple times per workday. 1   No 1
Fisher's Exact Test Chi-squared;Fisher's exact test Journal Article Power of testing proportions in small two-sample studies when sample sizes are equal. https://www.ncbi.nlm.nih.gov/pubmed/?term=8516594 Chi-squared and Fisher's exact test .   No .
Fmri fMRI Online Interactive Course Principles of fMRI 1 https://www.coursera.org/learn/functional-mri Per the course website, this course prepared by JHU Tcovers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data. . Any Yes .
Fraud Clinical Trials;Ethics;Fraud;Monitoring Journal Article The dangers of inferring treatment effects from observational data: a case study in HIV infection https://www.ncbi.nlm.nih.gov/pubmed/?term=11943438 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Fraud Clinical Trials;Ethics;Fraud;Monitoring Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Fraud Clinical Trials;Ethics;Fraud;Monitoring Journal Article When Was a 'Negative' Clinical Trail Big Enough? How Many Patients You Needed Depends on What You Found. https://www.ncbi.nlm.nih.gov/pubmed/?term=3985731 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Frequentist Statistics Bayesian statistics; frequentist statistics Journal Article Statistical Inference: The Big Picture https://www.ncbi.nlm.nih.gov/pubmed/21841892 Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction. 2   No 1
Friendman Kruskal-Wallis; Jonckheere-Terpstra; Friendman; nonparametric Journal Article Statistics review 10: Further nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/15153238 A previous review described analysis of variance, the method used to test for differences between more than two groups or treatments. However, in order to use analysis of variance, the observations are assumed to have been selected from Normally distributed populations with equal variance. The tests described in this review require only limited assumptions about the data. The Kruskal-Wallis test is the nonparametric alternative to one-way analysis of variance, which is used to test for differences between more than two populations when the samples are independent. The Jonckheere-Terpstra test is a variation that can be used when the treatments are ordered. When the samples are related, the Friedman test can be used. 2   No 2
Funnel Plot, Meta-Analysis funnel plot, meta-analysis Journal Article The case of the misleading funnel plot. https://www.ncbi.nlm.nih.gov/pubmed/16974018 funnel plot, meta-analysis .   No .
Futility Index Futility Index Journal Article The futility index. An approach to the cost-effective termination of randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3920906 Futility Index .   No .
Futility Testing Futility Testing Journal Article Comparison of futility monitoring guidelines using completed phase III oncology trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=27590208 Futility Testing .   No .
Futility Testing Futility Testing Software Instructions Early stopping in clinical trials and epidemiologic studies for "futility": conditional power versus sequential analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=12921928 Futility Testing .   No .
Futility Testing Futility Testing Journal Article Futility approaches to interim monitoring by data monitoring committees. https://www.ncbi.nlm.nih.gov/pubmed/17170036 Futility Testing .   No .
Futility Testing Futility Testing Journal Article A review of methods for futility stopping based on conditional power. https://www.ncbi.nlm.nih.gov/pubmed/16134130 Futility Testing .   No .
Futility Testing Futility Testing Journal Article A comment on futility monitoring. https://www.ncbi.nlm.nih.gov/pubmed/12161079 Futility Testing .   No .
Gee Clinical trials; GEE; logistic regression; correlated binary data Journal Article Sample size and power calculations with correlated binary data https://www.ncbi.nlm.nih.gov/pubmed/11384786 sample size formula for correlated binary data Control Clin Trials. 2001 Jun;22(3):211-27. 5 Any No 4
Gee GEE; Generalized Estimating Equations; epidemilogy Journal Article Statistical analysis of correlated data using generalized estimating equations: an orientation https://www.ncbi.nlm.nih.gov/pubmed/12578807 The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data. 2 Any No 2
Gee GEE Journal Article Extending the simple linear regression model to account for correlated responses: an introduction to generalized estimating equations and multi-level mixed modelling. https://www.ncbi.nlm.nih.gov/pubmed/?term=9670414 GEE .   No .
General power;sample size;general Journal Article Introduction to sample size determination and power analysis for clinical trials https://www.ncbi.nlm.nih.gov/pubmed/?term=7273794 Power/Sample size: general .   No .
General Linear Models General Linear Models Website General Linear Models (GLM) http://www.statsoft.com/Textbook/General-Linear-Models#basic_ideas General Linear Models .   No .
Generalized Estimating Equations GEE; Generalized Estimating Equations; epidemilogy Journal Article Statistical analysis of correlated data using generalized estimating equations: an orientation https://www.ncbi.nlm.nih.gov/pubmed/12578807 The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data. 2 Any No 2
Generalized Estimating Equations Generalized Estimating Equations Journal Article Statistical analysis of correlated data using generalized estimating equations: an orientation. https://www.ncbi.nlm.nih.gov/pubmed/12578807 Generalized Estimating Equations .   No .
Genomics genomics; statistics Online Interactive Course Genomics: the connection to public health practice http://cpheo1.sph.umn.edu/genomics/ Per the course website, the module will help to: Describe the overall knowledge of genomics and the applications of genomics in the public health field. Identify and access genomic resources and tools, including academic partnerships for public health practice. Identify practice opportunities for integrating genomics into public health practice and research. Discuss ethical, legal, social, and policy issues surrounding genomics and public health. Discuss the cultural challenges and varying perceptions of the value of genomics in the public health field. . Other No .
Genomics Genomics; GenomicTechnologies Online Interactive Course Introduction to Genomic Technologies https://www.coursera.org/learn/introduction-genomics Per the course website, this is course 1 of 8 in the Genomic Data Science Specialization prepared by JHU. This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. . Any Yes .
Genomics Genomics; GenomicTechnologies Online Interactive Course Genomic Data Science Specialization https://www.coursera.org/specializations/genomic-data-science Per the course website, this is a 8-course specialization prepared by JHU. This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics. . Any Yes .
Genomics Statistics; Genomics Online Interactive Course Statistics for Genomic Data Science https://www.coursera.org/learn/statistical-genomics Per the course website, this is course 7 of 8 in the Genomic Data Science Specialization prepared by JHU as an introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University. . Any Yes .
Genomics Genomics Online Interactive Course Bioconductor for Genomic Data Science https://www.coursera.org/learn/bioconductor Per the course website, this course, perpared by JHU, helps learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University. . Any Yes .
Genomictechnologies Genomics; GenomicTechnologies Online Interactive Course Introduction to Genomic Technologies https://www.coursera.org/learn/introduction-genomics Per the course website, this is course 1 of 8 in the Genomic Data Science Specialization prepared by JHU. This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. . Any Yes .
Genomictechnologies Genomics; GenomicTechnologies Online Interactive Course Genomic Data Science Specialization https://www.coursera.org/specializations/genomic-data-science Per the course website, this is a 8-course specialization prepared by JHU. This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, Python, R, Bioconductor, and Galaxy. The sequence is a stand alone introduction to genomic data science or a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics. . Any Yes .
Geographic Variations Geographic Variations Journal Article Geographic variation in the use of breast-conserving treatment for breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/1552911 Geographic Variations .   No .
Geographic Variations Geographic Variations Journal Article Geographic variation in expenditures for physicians' services in the United States. https://www.ncbi.nlm.nih.gov/pubmed/8429854 Geographic Variations .   No .
Gis GIS Online Interactive Course Geographic Information Systems (GIS) Specialization https://www.coursera.org/specializations/gis Per the course website, this 5-course specialiation prepared by UC Davis helps explore the world of spatial analysis and cartography with geographic information systems (GIS). In this class you will learn the basics of the industry . Any Yes .
Goodness Of Fit biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Graph Template Language SAS; Graph Template Language; GTL; LIFETEST Video Instruction Introduction to Graph Template Language (GTL) https://www.youtube.com/watch?v=Tu8jgNgLgfI Youtube video with an introduction to SAS Graph Template Language (GTL). GTL programming allows a user to customize the ODS graphics from SAS procedures. It is particularly useful for customizing the survival plots from LIFETEST. 4 SAS No 3
Graphics graphics Website Graphically Speaking http://blogs.sas.com/content/graphicallyspeaking/ A blog by SAS Institute which discusses new graphical features and tips. 1 SAS No 3
Graphics Graphics; Linear Models; Structural Equation Models; Categorical Models Website Michael Friendly's personal website http://www.datavis.ca/courses/index.php A variety resources and code for producing graphs and analyzing data 3 SAS, R No 3
Graphics tables; graphics Journal Article Describing data: statistical and graphical methods https://www.ncbi.nlm.nih.gov/pubmed/12461237 Methods are presented for summarizing data numerically, including presentation of data in tables and calculation of statistics for central tendency, variability, and distribution. Methods are also presented for displaying data graphically, including line graphs, bar graphs, histograms, and frequency polygons. . Any No .
Graphics graphics Journal Article Describing Data: Statistical and Graphical Methods https://www.ncbi.nlm.nih.gov/pubmed/12461237 An important step in any analysis is to describe the data by using descriptive and graphic methods. The author provides an approach to the most commonly used numeric and graphic methods for describing data. Methods are presented for summarizing data numerically, including presentation of data in tables and calculation of statistics for central tendency, variability, and distribution. Methods are also presented for displaying data graphically, including line graphs, bar graphs, histograms, and frequency polygons. The description and graphing of study data result in better analysis and presentation of data. 2   No 2
Graphics Probabilistic Graphical Models; Graphics; probability Online Interactive Course Probabilistic Graphical Models Specialization https://www.coursera.org/specializations/probabilistic-graphical-models Per the course website, this is a 3-course specialiation prepared by Stanford University to provide an understaing about probabilistic graphical models (PGMs) as a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. . Any Yes .
Group group;sequential;tests Journal Article A multiple testing procedure for clinical trials https://www.ncbi.nlm.nih.gov/pubmed/497341 Group Sequential Tests .   No .
Group group;sequential;tests Journal Article An aid to data monitoring in long-term clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7160189 Group Sequential Tests .   No .
Group group;sequential;test Journal Article Interim analyses in randomized clinical trials: ramifications and guidelines for practitioners. https://www.ncbi.nlm.nih.gov/pubmed/?term=3567306 Group Sequential Tests .   No .
Group group;sequential;tests Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Tests .   No .
Group group;sequential;test Journal Article Some extensions to a new approach for interim analysis in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1876784 Group Sequential Tests .   No .
Group group;sequential;test Journal Article Interim analysis in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1876782 Group Sequential Tests .   No .
Group group;sequential;test;survival analysis;data Other Stochastically Curtailed tests in Long-term Clinical Trials from Long Term Clinical Trials https://www.tandfonline.com/doi/abs/10.1080/07474948208836014 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article Statistics: the problem of examining accumulating data more than once. https://www.ncbi.nlm.nih.gov/pubmed/?term=4589874 Group Sequential Tests .   No .
Group group;sequential;tests;data;clinical trials Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Test .   No .
Group group;sequential;tests;design;analysis;data Journal Article Group sequential methods in the design and analysis of clinical trials https://www.jstor.org/stable/2335684?seq=1#metadata_info_tab_contents Group Sequential Tests .   No .
Group group;sequential;tests;clinical trials;data Journal Article Group sequential testing in clinical trials with multivariate observations: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8122047 Group Sequential Tests .   No .
Group group;sequential;tests Book Biopharmaceutical Statistics for Drug Development   Groups Sequential Tests .   No .
Group group;sequential;test;data Journal Article Monitoring Clinical Trial Data for Evidence of Adverse or Beneficial Treatment Effects. P.L. Canner   Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article Discrete sequential boundaries for clinical trials https://academic.oup.com/biomet/article/70/3/659/247777 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article On choosing the number of interim analyses in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7187080 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article One-sample multiple testing procedure for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7082756 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article Can early stopping procedures impact significantly on the efficiency of clinical trials without serious loss of information? https://www.ncbi.nlm.nih.gov/pubmed/?term=6528138 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article Symmetric group sequential test designs. https://www.ncbi.nlm.nih.gov/pubmed/?term=2675998 Group Sequential Tests .   No .
Group group;sequential;tests;data Journal Article Monitoring treatment differences in long-term clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=588655 Group Sequential Tests .   No .
Group Sequential Tests Group Sequential Tests Journal Article Statistical Approaches to Interim Monitoring of Medical Trials: A Review and Commentary https://projecteuclid.org/euclid.ss/1177012099 Group Sequential Tests .   No .
Group-Randomized Trials group-randomized trials; design; analysis Journal Article Design and analysis of group-randomized trials: a review of recent methodological developments. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998806 We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials. .   No .
Group-Randomized Trials design;analysis;group-randomized trials; methods Journal Article Design and analysis of group-randomized trials: a review of recent practices. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998802 We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published. .   No .
Gtl SAS; Graph Template Language; GTL; LIFETEST Video Instruction Introduction to Graph Template Language (GTL) https://www.youtube.com/watch?v=Tu8jgNgLgfI Youtube video with an introduction to SAS Graph Template Language (GTL). GTL programming allows a user to customize the ODS graphics from SAS procedures. It is particularly useful for customizing the survival plots from LIFETEST. 4 SAS No 3
Guidelines DSMB;data monitoring;guidelines Journal Article Data monitoring committees and interim monitoring guidelines. https://www.ncbi.nlm.nih.gov/pubmed/10503800 DSMB .   No .
Guides medical literature;guides Journal Article Users' Guides to the Medical Literature https://www.ncbi.nlm.nih.gov/pubmed/8411577 Reading Medical Literature .   No .
Guides reading medical literature;guides;diagnostic test;patient care Journal Article Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8309035 Reading medical literature .   No .
Harm reading medical literature;harm Journal Article Users' guides to the medical literature. IV. How to use an article about harm. Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=8182815 Reading Medical Literature .   No .
Hazard Function hazard function; competing risks; survival analysis Website Cause-Specific Analysis of Competing Risks Using the PHREG Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2159-2018.pdf Competing-risks analysis extends the capabilities of conventional survival analysis to deal with time-to-event data that have multiple causes of failure. Two regression modeling approaches can be used: one focuses on the cumulative incidence function (CIF) from a particular cause, and the other focuses on the cause-specific hazard function. These two quantities, unlike the hazard function and the survival function in conventional survival settings, are not connected through a simple one-to-one relationship. The Fine and Gray model extends the Cox model to analyze the cumulative incidence function but is often mistakenly assumed to be the only modeling technique available. The cause-specific approach that simultaneously models all the cause-specific hazard functions offers a more natural interpretation. SAS/STAT 14.3 includes updates to the PHREG procedure to perform the cause-specific analysis of competing risks. This paper describes how cause-specific hazard regression works and compares it to the Fine and Gray method. Examples illustrate how to interpret the models appropriately and how to obtain predicted cumulative incidence function. 4 SAS No 3
Hazard Ratios Hazard ratios Journal Article Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=24982461 Hazard ratios .   No .
Hazard Ratios Hazard ratios Journal Article Hazard ratio in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/15273082 Hazard ratios .   No .
Health Statistics reading medical literature;health statistics Journal Article Helping Doctors and Patients Make Sense of Health Statistics. https://www.ncbi.nlm.nih.gov/pubmed/?term=26161749 Reading Medical Literature on Health Statistics .   No .
Heart Valve medical practice problems;heart valve;clinical concern Journal Article Twenty-five-year experience with the Björk-Shiley convexoconcave heart valve: a continuing clinical concern. https://www.ncbi.nlm.nih.gov/pubmed/?term=15927993 Medical practice problems .   No .
Hierarchical multilevel;hierarchical;regress;data Journal Article Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures. https://www.ncbi.nlm.nih.gov/pubmed/?term=20949128 Multilevel/Hierarchical Regress .   No .
Hierarchical multilevel;hierarchical;regress;data Journal Article Tutorial in biostatistics. An introduction to hierarchical linear modelling. https://www.ncbi.nlm.nih.gov/pubmed/?term=10327531 Multilevel / Hierarchical Regress .   No .
Hierarchical multilevel;hierarchical;regress;data Journal Article Multi-level analysis in epidemiologic research on health behaviors and outcomes. https://www.ncbi.nlm.nih.gov/pubmed/1632420 Multilevel / Hierarchical Regress .   No .
Hierarchical Linear Model multilevel analysis; hierarchical linear model; random coefficients models; intra-class correlation coefficient; Book Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling https://www.amazon.com/Multilevel-Analysis-Introduction-Advanced-Modeling/dp/184920201X/ref=mt_paperback?_encoding=UTF8&me= Introduction to multilevel analysis, topics include hierarchical linear model, random coefficients models, how to calculate ICC, how much does a model explain, etc. 5 SAS, SPSS, R, M+, Yes 4
Hierarchical Models clustered trials; hierarchical models; power analysis Interactive Program Power analysis for group randomized trials https://ssc.researchmethodsresources.nih.gov/ssc/ NIH website about clustered randomized trials 2 Any No 1
Hypothesis Testing hypothesis testing; significance; P value Journal Article Statistics review 3: Hypothesis testing and P values https://www.ncbi.nlm.nih.gov/pubmed/12133182 The present review introduces the general philosophy behind hypothesis (significance) testing and calculation of P values. Guidelines for the interpretation of P values are also provided in the context of a published example, along with some of the common pitfalls. Examples of specific statistical tests will be covered in future reviews. 2   No .
Ibm SPSS; IBM; SPS programming Online Interactive Course IBM SPSS Statistics (Local) - Role: New User https://www-03.ibm.com/services/learning/ites.wss/zz/en/?pageType=page&c=V156959C29019C95 Pathways to courses provided by IBM to learn SPSS . SPSS No .
Icc Intraclass Correlation Coefficient; ICC Software to Download ICC9 a SAS Macro for Intralass Correlation Coefficient https://www.hsph.harvard.edu/donna-spiegelman/software/icc9/ By Donna Spiegelman this macro computes an Intraclass Correlation Coefficient and its 95% confidence interval. 3 SAS No 3
Iml IML; matrix programing; statistical computing Website The DO Loop blog http://blogs.sas.com/content/iml/ Rick Wicklin is a researcher in computational statistics at SAS and is a principal developer of PROC IML and SAS/IML Studio. This blog focuses on statistical programming. It discusses statistical and computational algorithms, statistical graphics, simulation, efficiency, and data analysis. 4 SAS No 4
Importing And Cleaning Data Data Science; SQL; importing and cleaning data; time series; machine learning; Online Interactive Course DataCamp - courses for Data Science https://www.datacamp.com/home This provides high quality courses on Data Science at both an introductory and advanced level using either R or Python. Some courses are free, others require a monthly fee to access. 1 R, Other Yes 2
Inference Estimation; Inference; Statistics Online Interactive Course Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation https://www.coursera.org/learn/statistical-reasoning-1 Per the course website, this course, prepared by JHU, provides a conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics. . Any Yes .
Inferential Statistics Inferential Statistics Online Interactive Course Inferential Statistics https://www.coursera.org/learn/inferential-statistics-intro Per the course website, this course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data . Any Yes .
Informatics Informatics; Statistics Online Interactive Course Materials Data Sciences and Informatics https://www.coursera.org/learn/material-informatics Per the course website, this course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges. . Any Yes .
Informatics Informatics Online Interactive Course Interprofessional Healthcare Informatics https://www.coursera.org/learn/health-informatics-professional Per the course website, this course, prepared by the University of Minnesota, is a tool to learn about infomatics in health care. Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network. . Any No .
Informatics certificate; BioInformatics; Informatics Online Interactive Course Public Health Informatics https://www.jhsph.edu/academics/certificate-programs/certificates-for-hopkins-and-non-degree-students/public-health-informatics.html Certificate in BioInformatics offered by JHU/ Bloomberg School of Public Health . Any, SAS Yes .
Informatively Censored Data informatively censored data Journal Article Multiple Imputation In Health-Care Databases: An Overview and some Applications https://www.ncbi.nlm.nih.gov/pubmed/?term=2057657 Informatively Censored Data .   No .
Informatively Censored Data informatively censored data Journal Article Methods For The Analysis Of Informatively Censored Longitudinal Data https://www.ncbi.nlm.nih.gov/pubmed/1480878 Informatively Censored Data .   No .
Intent To Treat intent to treat;intention to treat Journal Article Modified versus standard intention-to-treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross-sectional study. https://www.ncbi.nlm.nih.gov/pubmed/?term=21356072 Intention to treat .   No .
Intent To Treat intent to treat;intentions to treat Journal Article Intent-to-treat analysis and the problem of crossovers. An example from the Veterans Administration coronary bypass surgery study. https://www.ncbi.nlm.nih.gov/pubmed/?term=1999942 Intention to Treat .   No .
Intent To Treat intent to treat;intention to treat Journal Article Statistical considerations in the intent-to-treat principle. https://www.ncbi.nlm.nih.gov/pubmed/10822117 intention to treat .   No .
Intent To Treat intent to treat;intention to treat Journal Article Intention-to-treat analysis: implications for quantitative and qualitative research. https://www.ncbi.nlm.nih.gov/pubmed/?term=1468842 Intention to treat .   No .
Intent To Treat intent to treat; intention to treat Journal Article Intention to treat--who should use ITT? https://www.ncbi.nlm.nih.gov/pubmed/?term=8398686 intent to treat .   No .
Intent To Treat Intent to treat;intention to treat Journal Article Regression modelling strategies for improved prognostic prediction. https://www.ncbi.nlm.nih.gov/pubmed/?term=6463451 Intention to treat .   No .
Intent To Treat intent to treat Journal Article Analysis as-randomized and the problem of non-adherence: an example from the Veterans Affairs Randomized Trial of Coronary Artery Bypass Surgery. https://www.ncbi.nlm.nih.gov/pubmed/?term=8210821 Intention to treat .   No .
Intent To Treat intent to treat;intention to treat Journal Article Analysis of clinical trials by treatment actually received: is it really an option? https://www.ncbi.nlm.nih.gov/pubmed/?term=1947515 Intention to treat .   No .
Intent To Treat intent to treat;intention to treat Journal Article Intent-to-treat analysis for longitudinal studies with drop-outs. https://www.ncbi.nlm.nih.gov/pubmed/8962456 Intention to treat .   No .
Intent To Treat intent to treat; intention to treat Journal Article Intent-to-treat analysis for longitudinal studies with drop-outs. https://www.ncbi.nlm.nih.gov/pubmed/8962456 Intention to treat .   No .
Intent To Treat intent to treat; intention to treat Journal Article Intention-to-treat concept: A review. https://www.ncbi.nlm.nih.gov/pubmed/?term=21897887 Intention to treat .   No .
Intent-To-Treat analysis;clinical trials;randomization;intent-to-treat; Journal Article Intention-to-treat vs. on-treatment analyses of clinical trial data: experience from a study of pyrimethamine in the primary prophylaxis of toxoplasmosis in HIV-infected patients. ANRS 005/ACTG 154 Trial Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=9620807 Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. This paper presents several approaches used for analyzing data of a recent trial and the difficulties encountered in interpreting the results of each approach. Although exploratory analyses may yield clinically relevant information and useful clarifications in the evaluation of treatments, intention-to-treat remains the only interpretable analysis of clinical trials. .   No .
Intention To Treat intent to treat;intention to treat Journal Article Modified versus standard intention-to-treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross-sectional study. https://www.ncbi.nlm.nih.gov/pubmed/?term=21356072 Intention to treat .   No .
Intention To Treat intent to treat;intention to treat Journal Article Statistical considerations in the intent-to-treat principle. https://www.ncbi.nlm.nih.gov/pubmed/10822117 intention to treat .   No .
Intention To Treat intent to treat;intention to treat Journal Article Intention-to-treat analysis: implications for quantitative and qualitative research. https://www.ncbi.nlm.nih.gov/pubmed/?term=1468842 Intention to treat .   No .
Intention To Treat intent to treat; intention to treat Journal Article Intention to treat--who should use ITT? https://www.ncbi.nlm.nih.gov/pubmed/?term=8398686 intent to treat .   No .
Intention To Treat Intent to treat;intention to treat Journal Article Regression modelling strategies for improved prognostic prediction. https://www.ncbi.nlm.nih.gov/pubmed/?term=6463451 Intention to treat .   No .
Intention To Treat intent to treat;intention to treat Journal Article Analysis of clinical trials by treatment actually received: is it really an option? https://www.ncbi.nlm.nih.gov/pubmed/?term=1947515 Intention to treat .   No .
Intention To Treat intent to treat;intention to treat Journal Article Intent-to-treat analysis for longitudinal studies with drop-outs. https://www.ncbi.nlm.nih.gov/pubmed/8962456 Intention to treat .   No .
Intention To Treat intent to treat; intention to treat Journal Article Intent-to-treat analysis for longitudinal studies with drop-outs. https://www.ncbi.nlm.nih.gov/pubmed/8962456 Intention to treat .   No .
Intention To Treat intent to treat; intention to treat Journal Article Intention-to-treat concept: A review. https://www.ncbi.nlm.nih.gov/pubmed/?term=21897887 Intention to treat .   No .
Intentions To Treat intent to treat;intentions to treat Journal Article Intent-to-treat analysis and the problem of crossovers. An example from the Veterans Administration coronary bypass surgery study. https://www.ncbi.nlm.nih.gov/pubmed/?term=1999942 Intention to Treat .   No .
Interaclass Correlation interaclass correlation Journal Article Intraclass correlations: uses in assessing rater reliability. https://www.ncbi.nlm.nih.gov/pubmed/?term=18839484 Intraclass Correlation .   No .
Interaction interaction;synergy Journal Article Interaction in Epidemiologic Studies https://www.ncbi.nlm.nih.gov/pubmed/?term=736024 Interactions terms/synergy .   No .
Interactions interactions;synergy Journal Article Interaction and synergism. https://www.ncbi.nlm.nih.gov/pubmed/?term=7424894 Interactions terms/ synergy .   No .
Interactions interactions;synergy Journal Article The estimation of synergy or antagonism. https://www.ncbi.nlm.nih.gov/pubmed/1274952 Interactions terms/synergy .   No .
Interactions interactions;synergy Journal Article Synergy and antagonism in cause-effect relationships. https://www.ncbi.nlm.nih.gov/pubmed/?term=4841816 Interactions terms/synergy .   No .
Interpretation design;interpretation;clinical research Journal Article Adherence to treatment and health outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8250647 Adherence (or compliance) is the extent to which a person's behavior coincides with medical or health advice. Recent evidence indicates that patients who adhere to treatment, even when that treatment is a placebo, have better health outcomes than poorly adherent patients. Based on this evidence, we now believe that the outcomes of treatment are not solely attributable to the specific action of a drug, but may also depend on other nonspecific therapeutic effects. We consider the implications of these findings for the design and interpretation of clinical research as well as for the care of patients. .   No .
Interpretation compliance;randomization;design;interpretation;clinical trials Journal Article Compliance with an experimental drug regimen for treatment of asthma: its magnitude, importance, and correlates. https://www.ncbi.nlm.nih.gov/pubmed/?term=6389582 This paper reports on data from a double-blind, randomized controlled study of out-patient use of corticosteroids following an acute asthma attack. Issues related to compliance are examined, including: (1) the extent of non-compliance; (2) impact of non-compliance on interpreting the drug trial results; and (3) correlates of non-compliance. Of the 102 cases enrolled in the study, 25.5% were excluded from analysis because they were lost to follow-up (10.8%) or non-compliers (14.7%). Based on data for compliers, the drugs were found to reduce relapse rates and asthma symptomatology; when non-compliers were included in the analysis, the steroid drug appeared ineffective for reducing relapses and less effective for improving overall illness status. Examination of 24 potential correlates of compliance yielded a few significant associations, and only the "usual habit of compliance" correlation suggests an avenue for future action. The implications of the study findings for design and interpretation of clinical trials, as well as for improved management of chronic diseases, are discussed. .   No .
Interpretation compliance;clinical trials; interpretation Journal Article Role of patient compliance in clinical pharmacokinetics. A review of recent research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7988102 Until 1986 to 1987, the estimation of patient compliance with prescribed drug regimens in ambulatory care relied on methods that were biased either by their subjectivity or by the improvement in compliance that commonly occurs during the day or two prior to a scheduled examination, so called 'white-coat compliance'. In 1986 to 1987, 2 objective methods were developed: electronic monitoring and low-dose, slow-turnover chemical markers (digoxin or phenobarbital [phenobarbitone]) incorporated into dosage forms. While neither method is without limitations, both have enabled major advances in the understanding of patients' compliance with dosage regimens and, thus, the spectrum of drug exposure in ambulatory care. The new methods have also triggered not only a revival of interest in patient compliance and its determinants, but also new statistical approaches to interpreting the clinical correlates of widely variable drug administration, and thus drug exposure, in drug trials. The marker methods prove dose ingestion during the 3 to 7 days prior to blood sampling, but do not reveal the timing of doses. The electronic monitoring methods, i.e. time and date-stamping microcircuitry incorporated into drug packages, provide a continuous record of timing of presumptive doses throughout periods of many months, but do not prove dose ingestion. The electronic record has been judged robust enough to detect certain types of investigator fraud, and to support modelling projections of the complete time course of the plasma drug concentration during a trial. Both marker and electronic methods show that the predominant errors are those of omission, i.e. delays or omissions of scheduled doses. Patient interviews, diaries, and counts of returned, untaken doses have been shown by both marker and electronic monitoring methods to consistently and substantially to overestimate compliance. Monitoring of plasma drug concentrations also overestimates compliance, because white-coat compliance is prevalent, and the pharmacokinetic turnover of most drugs is rapid enough that measured concentrations of drug in plasma reflect only drug administration during the period of white-coat compliance. Thus, compliance is a great deal poorer in clinical trials than has been revealed by the older methods. The long-standing underestimation of poor compliance in drug trials has many implications for the interpretation of drug trials, for optimal dose estimation, for the interpretation of failed drug therapy, and for accurate labelling of prescription drugs. .   No .
Interpretation interpretation; bias;study design Journal Article Why most published research findings are false. https://www.ncbi.nlm.nih.gov/pubmed/16060722 Interpretation, bias, study design .   No .
Interpreting Results compliance;methodology;interpreting results Journal Article Patient compliance and medical research: issues in methodology. https://www.ncbi.nlm.nih.gov/pubmed/?term=8164085 Compliance with medication and medical appointments is presumed to have a critical influence on outcomes of medical interventions. Readers of the medical literature should consider how compliance was measured and analyzed when interpreting the results of clinical trials. Table 4 suggests criteria for critical appraisal of compliance-related issues in reports of clinical trials. .   No .
Interpreting Results sample size;clinical study;interpreting results Journal Article Sample size nomograms for interpreting negative clinical studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6881780 In recent years there has been increasing attention to the appropriate interpretation of a clinical study. One special concern has been the difficulty inherent in interpreting studies that were not statistically significant: Was the sample size sufficient to detect a clinically important effect if, in fact, it existed? This concern is further complicated because readers may have differing opinions of what size effect is clinically important. A pair of sample size nomograms has been developed, using common levels of statistical significance, to assist in this interpretation. The nomograms are intended to provide the clinician with a handy and easy-to-use reference for ascertaining whether an apparently negative study has a sample size adequate to detect reliably any difference between treatment groups that the clinician believes is clinically important. Examples are provided to show these principles and the use of the nomograms in interpreting negative studies. .   No .
Interrupted Time Series interrupted time series; research design Journal Article Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis https://www.ncbi.nlm.nih.gov/pubmed/26058820 Interrupted time series analysis is a quasi-experimental design that can evaluate an intervention e ect, using longitudinal data. The advantages, disadvantages, and underlying assumptions of various modelling approaches are discussed using published examples 3   No 2
Intraclass Correlation Coefficient Intraclass Correlation Coefficient; ICC Software to Download ICC9 a SAS Macro for Intralass Correlation Coefficient https://www.hsph.harvard.edu/donna-spiegelman/software/icc9/ By Donna Spiegelman this macro computes an Intraclass Correlation Coefficient and its 95% confidence interval. 3 SAS No 3
Introduction To Biostatistics introduction to biostatistics Book Fundamentals of Biostatistics https://www.amazon.com/Fundamentals-Biostatistics-Bernard-Rosner/dp/130526892X/ref=sr_1_1?s=books&ie=UTF8&qid=1515103940&sr=1-1&keywords=fundamentals+of+biostatistics This book can be used for clinicians to understand basic concepts and methods in Biostatistics. 2 Any Yes 1
Jackknife resampling; bootstrap; jackknife; cross-validation; simulation Website Don't Be Loopy: Re-Sampling and Simulation the SAS Way http://www2.sas.com/proceedings/forum2007/183-2007.pdf An excellent paper by David Cassell presented the SAS user group about how to program resampling statistics in SAS. The most common way that people do simulations and re-sampling plans in SAS® is, in fact, the slow and awkward way. People tend to think in terms of a huge macro loop wrapped around a piece of SAS code, with additional chunks of code to get the outputs of interest and then to weld together the pieces from each iteration. But SAS is designed to work with by-processing, so there is a better way. A faster way. This paper will show a simpler way to perform bootstrapping, jackknifing, cross-validation, and simulations from established populations. It is simpler and more efficient to get SAS to build all the iterations in one long SAS data set, then use by-processing to do all the computations at once. This lets us use SAS features to gather automatically the information from all the iterations, for simpler computations afterward. 4 SAS No 3
Jonckheere-Terpstra Kruskal-Wallis; Jonckheere-Terpstra; Friendman; nonparametric Journal Article Statistics review 10: Further nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/15153238 A previous review described analysis of variance, the method used to test for differences between more than two groups or treatments. However, in order to use analysis of variance, the observations are assumed to have been selected from Normally distributed populations with equal variance. The tests described in this review require only limited assumptions about the data. The Kruskal-Wallis test is the nonparametric alternative to one-way analysis of variance, which is used to test for differences between more than two populations when the samples are independent. The Jonckheere-Terpstra test is a variation that can be used when the treatments are ordered. When the samples are related, the Friedman test can be used. 2   No 2
Kaplan-Meier survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Kaplan-Meier survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
Kaplan-Meier survival data analysis;competing risks;Kaplan-Meier;method; Journal Article A note on competing risks in survival data analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=15305188 Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events. .   No .
Kaplan-Meier probability;competing risks;Kaplan-Meier; Journal Article Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. https://www.ncbi.nlm.nih.gov/pubmed/?term=10204198 A topic that has received attention in both the statistical and medical literature is the estimation of the probability of failure for endpoints that are subject to competing risks. Despite this, it is not uncommon to see the complement of the Kaplan-Meier estimate used in this setting and interpreted as the probability of failure. If one desires an estimate that can be interpreted in this way, however, the cumulative incidence estimate is the appropriate tool to use in such situations. We believe the more commonly seen representations of the Kaplan-Meier estimate and the cumulative incidence estimate do not lend themselves to easy explanation and understanding of this interpretation. We present, therefore, a representation of each estimate in a manner not ordinarily seen, each representation utilizing the concept of censored observations being 'redistributed to the right.' We feel these allow a more intuitive understanding of each estimate and therefore an appreciation of why the Kaplan-Meier method is inappropriate for estimation purposes in the presence of competing risks, while the cumulative incidence estimate is appropriate. .   No .
Kaplan-Meier Kaplan-Meier; probability;competing risks Journal Article Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? https://www.ncbi.nlm.nih.gov/pubmed/?term=8516591 In the context of competing risks the Kaplan-Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two-sample test statistics are also presented. .   No .
Kappa Statistic Kappa Statistic Journal Article Misinterpretation and misuse of the kappa statistic. https://www.ncbi.nlm.nih.gov/pubmed/3300279 Kappa Statistic .   No .
Kappa Statistic Kappa Statistic Journal Article Behavior and interpretation of the kappa statistic: resolution of the two paradoxes. https://www.ncbi.nlm.nih.gov/pubmed/?term=behavior+and+interpretation+of+the+k+statistic%3A+resolution+of+the+two+paradoxes Kappa Statistic .   No .
Kappa Statistic Kappa Statistic Journal Article Acceptable values of kappa for comparison of two groups. https://www.ncbi.nlm.nih.gov/pubmed/1570823 Kappa Statistic .   No .
Kappa Statistic Kappa Statistic Journal Article Kappa and attenuation of the odds ratio. https://www.ncbi.nlm.nih.gov/pubmed/2078611 Kappa Statistic .   No .
Kruskal-Wallis Kruskal-Wallis; Jonckheere-Terpstra; Friendman; nonparametric Journal Article Statistics review 10: Further nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/15153238 A previous review described analysis of variance, the method used to test for differences between more than two groups or treatments. However, in order to use analysis of variance, the observations are assumed to have been selected from Normally distributed populations with equal variance. The tests described in this review require only limited assumptions about the data. The Kruskal-Wallis test is the nonparametric alternative to one-way analysis of variance, which is used to test for differences between more than two populations when the samples are independent. The Jonckheere-Terpstra test is a variation that can be used when the treatments are ordered. When the samples are related, the Friedman test can be used. 2   No 2
Laird longitudinal;study design;data analysis;linear models;laird;ware Journal Article Families of lines: random effects in linear regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/3379003 Longitudinal Studies Design: data analysis: linear (Laird/Ware) Models .   No .
Laird Longitudinal;Study Design;data analysis;linear Models;Laird;Ware Journal Article The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3526500 Longitudinal Studies Design;data analysis;linear (Laird/Ware) Models .   No .
Laird Ware longitudinal data analysis;linear models;laird ware Journal Article Mild senile dementia of the Alzheimer type. 4. Evaluation of intervention. https://www.ncbi.nlm.nih.gov/pubmed/?term=1637132 Longitudinal Data Analysis: linear (Laird/Ware)Models .   No .
Large Data observational data; effect sizes; large data Journal Article Tips for Analyzing Large Data Sets From the JAMA Surgery Statistical Editors https://www.ncbi.nlm.nih.gov/pubmed/29617520 With the advent of administrative databases and patient registries, big data is increasingly accessible to researchers. The large sample size of these data sets make the study of rare outcomes easier and provide the potential to determine national estimates and regional variations. As such, the JAMA Surgery editors and reviewers have seen more submissions using big data to answer clinical and policy-related questions. However, no database is completely free of bias and measurement error. With bigger data, random signals may denote statistical significance, and precision may be incorrectly inferred because of narrow confidence intervals. While many principles apply to all studies, the importance of these methodological issues is amplified in large, complex data sets. 1 Any No 1
Large Trials meta-analyses; large trials;clinical research methods Journal Article Issues in comparisons between meta-analyses and large trials. https://www.ncbi.nlm.nih.gov/pubmed/9546568 meta-analyses and large trials .   No .
Latent Variable Models network analysis; psychopathology; latent variable models; psychometrics Journal Article Network analysis: an integrative approach to the structure of psychopathology. https://www.ncbi.nlm.nih.gov/pubmed/23537483 In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry -> insomnia -> fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions. 3   No 1
Least Squares Least Squares; Data modeling; Statistics Online Interactive Course Advanced Linear Models for Data Science 1: Least Squares https://www.coursera.org/learn/linear-models Per the course website, this course is prepared by JHU to help provide: A basic understanding of linear algebra and multivariate calculus; A basic understanding of statistics and regression models; At least a little familiarity with proof based mathematics; and Basic knowledge of the R programming language. . Any Yes .
Life Table survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
Lifetest SAS; Graph Template Language; GTL; LIFETEST Video Instruction Introduction to Graph Template Language (GTL) https://www.youtube.com/watch?v=Tu8jgNgLgfI Youtube video with an introduction to SAS Graph Template Language (GTL). GTL programming allows a user to customize the ODS graphics from SAS procedures. It is particularly useful for customizing the survival plots from LIFETEST. 4 SAS No 3
Linear Models Graphics; Linear Models; Structural Equation Models; Categorical Models Website Michael Friendly's personal website http://www.datavis.ca/courses/index.php A variety resources and code for producing graphs and analyzing data 3 SAS, R No 3
Linear Models Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Linear Models Linear Models; Statistics Online Interactive Course Advanced Linear Models for Data Science 2: Statistical Linear Models https://www.coursera.org/learn/linear-models-2 Per the course website, this course, prepared by JHU, helps to provide a basic understanding of linear algebra and multivariate calculus, a basic understanding of statistics and regression models, at least a little familiarity with proof based mathematics, and basic knowledge of the R programming language. . Any Yes .
Linear Models Linear Models; Multivariate Analysis; Regression Analysis Video Instruction Introduction to Model Selection https://www.youtube.com/watch?v=VB1qSwoF-l0&list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I This 5-minute video provides an overview to use of forward selection and backward elimination in developing a multivariable linear regression model. 2   No 1
Linear Models longitudinal data analysis;linear models;laird ware Journal Article Mild senile dementia of the Alzheimer type. 4. Evaluation of intervention. https://www.ncbi.nlm.nih.gov/pubmed/?term=1637132 Longitudinal Data Analysis: linear (Laird/Ware)Models .   No .
Linear Models longitudinal;study design;data analysis;linear models;laird;ware Journal Article Families of lines: random effects in linear regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/3379003 Longitudinal Studies Design: data analysis: linear (Laird/Ware) Models .   No .
Linear Models Longitudinal;Study Design;data analysis;linear Models;Laird;Ware Journal Article The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3526500 Longitudinal Studies Design;data analysis;linear (Laird/Ware) Models .   No .
Linear Multivariate Model power calculations;sample size;linear multivariate model;repeated measures Journal Article Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. https://www.ncbi.nlm.nih.gov/pubmed/?term=24790282 Power and sample size .   No .
Linear Regression biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Linear Regression linear regression; correlation Journal Article Statistics review 7: Correlation and regression https://www.ncbi.nlm.nih.gov/pubmed/14624685 The present review introduces methods of analyzing the relationship between two quantitative variables. The calculation and interpretation of the sample product moment correlation coefficient and the linear regression equation are discussed and illustrated. Common misuses of the techniques are considered. Tests and confidence intervals for the population parameters are described, and failures of the underlying assumptions are highlighted. 2   No 2
Linear Regression Chi-square test; t-test; linear regression; logistic regression Book OpenIntro Statistics https://www.openintro.org/stat/textbook.php?stat_book=os An introductory textbook and on-line videos about biostatistics, including linear and logistic regression. The PDF is freely downloadable, but a donation is requested. 2 Any No 1
Linear Regression Linear Regression; Modeling Online Interactive Course Linear Regression and Modeling https://www.coursera.org/learn/linear-regression-model Per the course website, this course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio. . Any Yes .
Log Rank survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Logistic Models Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Logistic Regression biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Logistic Regression logistic regression Journal Article Statistics review 14: Logistic regression https://www.ncbi.nlm.nih.gov/pubmed/15693993 This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered. 2   No 2
Logistic Regression Chi-square test; t-test; linear regression; logistic regression Book OpenIntro Statistics https://www.openintro.org/stat/textbook.php?stat_book=os An introductory textbook and on-line videos about biostatistics, including linear and logistic regression. The PDF is freely downloadable, but a donation is requested. 2 Any No 1
Logistic Regression Clinical trials; GEE; logistic regression; correlated binary data Journal Article Sample size and power calculations with correlated binary data https://www.ncbi.nlm.nih.gov/pubmed/11384786 sample size formula for correlated binary data Control Clin Trials. 2001 Jun;22(3):211-27. 5 Any No 4
Logistic Regression logistic regression;analysis Journal Article Long-term metered-dose inhaler adherence in a clinical trial. The Lung Health Study Research Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=7633711 Poor adherence to medication regimens is a well-documented phenomenon in clinical practice and an ever-present concern in clinical trials. Results of multiple logistic regression analysis indicate that the best compliance was found in participants who were married, older, white, had more severe airways obstruction, less shortness of breath, and fewer hospitalizations, and who had not been confined to bed for respiratory illnesses.(ABSTRACT TRUNCATED AT 250 WORDS). .   No .
Logistic Regression Logistic Regression Journal Article Problems due to small samples and sparse data in conditional logistic regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/10707923 Logistic Regression .   No .
Logistic Regression Logistic Regression Journal Article Explained variation for logistic regression. https://www.ncbi.nlm.nih.gov/pubmed/8896134 Logistic Regression .   No .
Logistic Regression Logistic regression Journal Article On the efficacy of the rank transformation in stepwise logistic and discriminant analysis. https://www.ncbi.nlm.nih.gov/pubmed/8446809 Logistic regression .   No .
Logistic Regression Logistic regression Journal Article Estimation of the multivariate logistic risk function: a comparison of the discriminant function and maximum likelihood approaches. https://www.ncbi.nlm.nih.gov/pubmed/?term=estimation+of+the+multivariate+logistic+risk+function%3A+a+comparison+of+the+discriminant+function+and+maximum+likelihood+approaches Logistic regression .   No .
Logistic Regression logistic regression;ordinal multinomial logistic Other Multinomial and ordinal logistic regression using PROC LOGISTIC https://www.scribd.com/document/199770869/Multinomial-and-ordinal-logistic-regression-using-PROC-LOGISTIC Logistic Regression .   No .
Logistic Regression. power analysis; bias (epidemiology); model adequacy; type I error; cox proportional hazards models; logistic regression. Journal Article Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression https://academic.oup.com/aje/article/165/6/710/63906 Commentary and simulation about back-of-the-envelope power analyses for logistic and Cox regression. 2 Any No 1
Logrank Test survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
Longitudianl Data Analysis longitudianl data analysis;paired eyes Journal Article Methods to quantify the relation between disease progression in paired eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=10853635 Longitudinal Data Analysis: Paired Eyes .   No .
Longitudinal longitudinal;study design;data analysis;linear models;laird;ware Journal Article Families of lines: random effects in linear regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/3379003 Longitudinal Studies Design: data analysis: linear (Laird/Ware) Models .   No .
Longitudinal Longitudinal;Study Design;data analysis;linear Models;Laird;Ware Journal Article The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3526500 Longitudinal Studies Design;data analysis;linear (Laird/Ware) Models .   No .
Longitudinal longitudinal;study design Journal Article Planning a longitudinal study. II. Frequency of measurement and study duration. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759581 Longitudinal Studies Design .   No .
Longitudinal Data Analysis longitudinal data analysis;multivariate Journal Article Estimating incidence and diagnostic error rates for bivariate progressive processes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8117898 Longitudinal Data Analysis:Multivariate .   No .
Longitudinal Data Analysis longitudinal data analysis;multivariate Journal Article A multivariate growth curve model for pregnancy. https://www.ncbi.nlm.nih.gov/pubmed/1912265 Longitudinal Data Analysis: Multivariate .   No .
Longitudinal Data Analysis longitudinal data analysis;multivariate Journal Article Multivariate methods for clustered ordinal data with applications to survival analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=9044526 Longitudinal Data Analysis;multivariate .   No .
Longitudinal Data Analysis longitudinal data analysis;linear models;laird ware Journal Article Mild senile dementia of the Alzheimer type. 4. Evaluation of intervention. https://www.ncbi.nlm.nih.gov/pubmed/?term=1637132 Longitudinal Data Analysis: linear (Laird/Ware)Models .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Advances in analysis of longitudinal data. https://www.ncbi.nlm.nih.gov/pubmed/?term=20192796 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Modelling covariance structure in the analysis of repeated measures data. https://www.ncbi.nlm.nih.gov/pubmed/?term=10861779 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Statistical analysis of repeated measures data using SAS procedures. https://www.ncbi.nlm.nih.gov/pubmed/9581947 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. https://www.ncbi.nlm.nih.gov/pubmed/?term=9351170 Longitudinal data analysis .   No .
Longitudinal Data Analysis Longitudinal data analysis Journal Article An overview of methods for the analysis of longitudinal data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1480876 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Comparison of methods for the analysis of longitudinal interval count data. https://www.ncbi.nlm.nih.gov/pubmed/8210830 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Longitudinal data analysis for discrete and continuous outcomes. https://www.ncbi.nlm.nih.gov/pubmed/3719049 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Reliability of estimates of changes in mental status test performance in senile dementia of the Alzheimer type. https://www.ncbi.nlm.nih.gov/pubmed/?term=2348211 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article The analysis of event history data: a review of progress and outstanding problems. https://www.ncbi.nlm.nih.gov/pubmed/?term=3413364 Longitudinal Data Analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Risk factors for genetic typing and detection in retinitis pigmentosa. https://www.ncbi.nlm.nih.gov/pubmed/?term=6966889 Longitudinal Data analysis .   No .
Longitudinal Data Analysis longitudinal data analysis Journal Article Mixed Poisson likelihood regression models for longitudinal interval count data. https://www.ncbi.nlm.nih.gov/pubmed/3358988 Longitudinal Data Analysis .   No .
Longitudinal Studies longitudinal studies;design Journal Article Planning a longitudinal study. I. Sample size determination. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759580 Longitudinal Studies - Design .   No .
Longitudinal Studies longitudinal studies;design Journal Article Planning a longitudinal study. II. Frequency of measurement and study duration. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759581 Longitudinal Studies-Design .   No .
M+ SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Machine Learning Machine learning Video Instruction Machine Learning (Stanford) https://www.youtube.com/watch?v=UzxYlbK2c7E Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. 4   No 4
Machine Learning Data Science; SQL; importing and cleaning data; time series; machine learning; Online Interactive Course DataCamp - courses for Data Science https://www.datacamp.com/home This provides high quality courses on Data Science at both an introductory and advanced level using either R or Python. Some courses are free, others require a monthly fee to access. 1 R, Other Yes 2
Mann-Whitney Test sign test; Wilcoxon signed rank test; Wilcoxon rank sum test; Mann-Whitney test; nonparametric tests Journal Article Statistics review 6: Nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/12493072 The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed. 2   No .
Master's Degree master's degree Online Interactive Course Master of Public Health https://www.jhsph.edu/academics/degree-programs/master-of-public-health/program-overview/online-part-time.html Master of Public Health (part-time online) offered by JHU/ Bloomberg School of Public Health . Any, SAS Yes .
Masters Programs masters programs; certificate program Online Interactive Course Penn State Online Applied Statistics https://onlinecourses.science.psu.edu/statprogram/ Penn State offers both a certificate and a masters degree program in applied statistics. All coursework can be taken online. Individual courses can also be taken. 2 SAS, R Yes 2
Matching matching;community based;studies Journal Article The efficiency of the matched-pairs design of the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/9129857 Matching Community Based Studies .   No .
Matching matching;community based;studies;case control Journal Article The merits of matching in community intervention trials: a cautionary tale. https://www.ncbi.nlm.nih.gov/pubmed/?term=9265698 Matching Community Based Studies .   No .
Matching matching;community based;studies;case control studies Journal Article The effect of matching on the power of randomized community intervention studies. https://www.ncbi.nlm.nih.gov/pubmed/8456215 Matching Community Based Studies .   No .
Matching matching;case control studies Journal Article The Comparison of Percentages in Matched Samples https://www.ncbi.nlm.nih.gov/pubmed/?term=14801052 Matching in case control studies .   No .
Matching matching;case control studies Journal Article he matched pairs design in the case of all-or-none responses. https://www.ncbi.nlm.nih.gov/pubmed/?term=5683874 Matching in case control studies .   No .
Matching matching;case control studies Journal Article Matching and design efficiency in retrospective studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=5416244 Matching in case control studies .   No .
Matching matching;case control studies Journal Article Estimating the utility of matching in case-control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6501548 Matching in case control studies .   No .
Matching matching;case control studies Journal Article A comparison of different matching designs in case-control studies: an empirical example using continuous exposures, continuous confounders and incidence of myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341869 Matching in case control studies .   No .
Math Math; Statistics Online Interactive Course Data Science Math Skills https://www.coursera.org/learn/datasciencemathskills Per the course website, learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes . Any Yes .
Mathematical Biostatistic Mathematical Biostatistic Online Interactive Course Mathematical Biostatistics Boot Camp 2 https://www.coursera.org/learn/biostatistics-2 Per the course website, this course prepared by JHU helps learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. . Any Yes .
Matrix Programing IML; matrix programing; statistical computing Website The DO Loop blog http://blogs.sas.com/content/iml/ Rick Wicklin is a researcher in computational statistics at SAS and is a principal developer of PROC IML and SAS/IML Studio. This blog focuses on statistical programming. It discusses statistical and computational algorithms, statistical graphics, simulation, efficiency, and data analysis. 4 SAS No 4
Mean distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Mean regression;mean Journal Article Estimating the effect of regression toward the mean under stochastic censoring. https://www.ncbi.nlm.nih.gov/pubmed/?term=8109577 Regression to the Mean .   No .
Mean regression;mean Journal Article How much of the placebo 'effect' is really statistical regression? https://www.ncbi.nlm.nih.gov/pubmed/6369471 Regression to the Mean .   No .
Measure Of Association contingency table; measure of association; chi square; proportions Journal Article Statistics review 8: Qualitative data - tests of association https://www.ncbi.nlm.nih.gov/pubmed/14975045 This review introduces methods for investigating relationships between two qualitative (categorical) variables. The chi square test of association is described, together with the modifications needed for small samples. The test for trend, in which at least one of the variables is ordinal, is also outlined. Risk measurement is discussed. The calculation of confidence intervals for proportions and differences between proportions are described. Situations in which samples are matched are considered. 2   No .
Measures repeated;measures;analysis Journal Article Analysis of serial measurements in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2106931 repeated measures analysis .   No .
Measures Of Location distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Measures Of Variability distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Median distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Mediator causal mediation; mediator; observational data; counterfactual analysis; Website Causal Mediation Analysis with the CAUSALMED Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1991-2018.pdf Important policy and health care decisions often depend on understanding the direct and indirect (mediated) effects of a treatment on an outcome. For example, does a youth program directly reduce juvenile delinquent behavior, or does it indirectly reduce delinquent behavior by changing the moral and social values of teenagers? Or, for example, is a particular gene directly responsible for causing lung cancer, or does it have an indirect (mediated) effect through its influence on smoking behavior? Causal mediation analysis deals with the mechanisms of causal treatment effects, and it estimates direct and indirect effects. A treatment variable is assumed to have causal effects on an outcome variable through two pathways: a direct pathway and a mediated (indirect) pathway through a mediator variable. This paper introduces the CAUSALMED procedure, new in SAS/STAT® 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework. The paper also defines these causal mediation and related effects in terms of counterfactual outcomes and describes the assumptions that are required for unbiased estimation. Examples illustrate the ideas behind causal mediation analysis and the applications of the CAUSALMED procedure. 4 SAS No 3
Medical Bankruptsy Medical Bankruptsy Journal Article Medical bankruptcy in the United States, 2007: results of a national study. https://www.ncbi.nlm.nih.gov/pubmed/19501347 Medical Bankruptsy .   No .
Medical Literature medical literature;evaluation;clinical research Journal Article Critical Evaluation of Clinical Research https://www.ncbi.nlm.nih.gov/pubmed/?term=7811181 Reading Medical Literature .   No .
Medical Literature medical literature;guides Journal Article Users' Guides to the Medical Literature https://www.ncbi.nlm.nih.gov/pubmed/8411577 Reading Medical Literature .   No .
Medical Practice Problems medical practice problems Journal Article Geographic variation in expenditures for physicians' services in the United States. Geographic variation in expenditures for physicians' services in the United States. Medical Practice Problems .   No .
Medical Practice Problems medical practice problems;heart valve;clinical concern Journal Article Twenty-five-year experience with the Björk-Shiley convexoconcave heart valve: a continuing clinical concern. https://www.ncbi.nlm.nih.gov/pubmed/?term=15927993 Medical practice problems .   No .
Medical Practice Problems Medical practice problems Journal Article The Björk-Shiley 70 degree convexo-concave prosthesis strut fracture problem (present state of information). https://www.ncbi.nlm.nih.gov/pubmed/?term=2440138 Medical practice problems .   No .
Medical Practice Problems medical practice problems;strut fracture;Bjork-Shiley; Journal Article Risk of strut fracture of Björk-Shiley valves. https://www.ncbi.nlm.nih.gov/pubmed/1346279 Medical practice problems .   No .
Medical Practice Problems medical practice problems Journal Article Prophylactic lidocaine for suspected acute myocardial infarction? https://www.ncbi.nlm.nih.gov/pubmed/1344106 Medical practice problems .   No .
Meta Analyses meta analyses; randomized trials Journal Article A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=1535110 To examine the temporal relationship between accumulating data from randomized control trials of treatments for myocardial infarction and the recommendations of clinical experts writing review articles and textbook chapters. .   No .
Meta Analysis biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Meta Analysis meta analysis Journal Article Bias in meta-analysis detected by a simple, graphical test. https://www.ncbi.nlm.nih.gov/pubmed/?term=9310563 Meta-analysis problems .   No .
Meta Analysis meta analysis;cardiology Journal Article Effect of intravenous nitrates on mortality in acute myocardial infarction: an overview of the randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/2896919 Meta Analysis Papers-Cardiology .   No .
Meta Analysis meta analysis;cardiology Journal Article Effects of prophylactic lidocaine in suspected acute myocardial infarction. An overview of results from the randomized, controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/3047448 Meta Analysis - Cardiology .   No .
Meta Analysis meta analysis;cardiology Journal Article The effect of low-dose warfarin on the risk of stroke in patients with nonrheumatic atrial fibrillation. https://www.ncbi.nlm.nih.gov/pubmed/2233931 Meta analysis - cardiology .   No .
Meta Analysis meta analysis; cardiology Journal Article The effect of warfarin on mortality and reinfarction after myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/2194126 Meta analysis - cardiology .   No .
Meta Analysis meta analysis; cardiology Journal Article Meta-analytic evidence against prophylactic use of lidocaine in acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=PMID%3A+2688587 Meta Analysis - Cardiology .   No .
Meta Analysis meta analysis; cardiology Journal Article Cumulative meta-analysis of therapeutic trials for myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/1614465 Meta analysis - Cardiology .   No .
Meta-Analyses meta-analyses; large trials;clinical research methods Journal Article Issues in comparisons between meta-analyses and large trials. https://www.ncbi.nlm.nih.gov/pubmed/9546568 meta-analyses and large trials .   No .
Meta-Analysis clinical trials;meta-analysis Journal Article Clinical trials and meta-analysis. What do they do for us? https://www.ncbi.nlm.nih.gov/pubmed/?term=1614470 Clinical trials and meta-analysis. What do they do for us? .   No .
Meta-Analysis meta-analysis;compliance Journal Article Meta-analysis adjusting for compliance: the example of screening for breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=1432006 Randomized controlled trials are usually analysed by the group to which the patient was randomized, i.e. by "intention-to-treat", regardless of the degree of compliance. However, the "explanatory" effect, i.e. the effect that would occur if we had 100% compliance, is often of interest. This "explanatory" effect is diluted by poor compliance, and hence meta-analyses should ideally avoid both the heterogeneity of effect due to variation in compliance rates among studies, and the undeserved weight given to trials with poor compliance. Newcombe's deattenuation method, which adjusts estimates for the degree of compliance, is extended and applied to a meta-analysis of the five reported randomized controlled trials of mammographic screening. Compliance with screening varied across studies: from 61 to 93% assigned to screening had one or more mammograms. The adjusted estimate of the reduction in breast cancer mortality at 9 years follow-up is 0.37 (95% confidence interval: 0.21, 0.49). .   No .
Meta-Analysis methodology;bias;meta-analysis Journal Article Methodology and overt and hidden bias in reports of 196 double-blind trials of nonsteroidal antiinflammatory drugs in rheumatoid arthritis. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702836%5Buid%5D Important design aspects were decreasingly reported in NSAID trials over the years, whereas the quality of statistical analysis improved. In half of the trials, the effect variables in the methods and results sections were not the same, and the interpretation of the erythrocyte sedimentation rate in the reports seemed to depend on whether a significant difference was found. Statistically significant results appeared in 93 reports (47%). In 73 trials they favored only the new drug, and in 8 only the active control. All 39 trials with a significant difference in side effects favored the new drug. Choice of dose, multiple comparisons, wrong calculation, subgroup and within-groups analyses, wrong sampling units (in 63% of trials for effect variables, in 23% for side effects), change in measurement scale before analysis, baseline difference, and selective reporting of significant results were some of the verified or possible causes for the large proportion of results that favored the new drug. Doubtful or invalid statements were found in 76% of the conclusions or abstracts. Bias consistently favored the new drug in 81 trials, and the control in only one trial. It is not obvious how a reliable meta-analysis could be done in these trials. .   No .
Meta-Analysis randomized controlled trial; bias;meta-analysis Journal Article Systematic review of the empirical evidence of study publication bias and outcome reporting bias. https://www.ncbi.nlm.nih.gov/pubmed/?term=18769481 The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention. Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials. .   No .
Meta-Analysis Meta-analysis;State-of-the-Science Journal Article Meta-analysis: State-of-the-Science https://www.ncbi.nlm.nih.gov/pubmed/?term=7977286 Meta-analysis: State-of-the-Science .   No .
Meta-Analysis statistical significance; meta-analysis Journal Article Is everything we eat associated with cancer? A systematic cookbook review. https://www.ncbi.nlm.nih.gov/pubmed/?term=23193004 statistical significance; meta-analysis .   No .
Meta-Analysis publication bias;meta-analysis;data Journal Article Should unpublished data be included in meta-analyses? Current convictions and controversies https://www.ncbi.nlm.nih.gov/pubmed/8492400 Publication Bias .   No .
Metaanalysis MetaAnalysis Journal Article Can Meta-Analysis Make Policy? https://www.ncbi.nlm.nih.gov/pubmed/?term=7673676 MetaAnalysis .   No .
Metaanalysis MetaAnalysis; Methodological Issues Journal Article Detecting and describing heterogeneity in meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=9595615 MetaAnalysis Methodological Issues .   No .
Metaanalysis MetaAnalysis Journal Article Discordance between meta-analysis and large-scale randomized, controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7486471 MetaAnalysis .   No .
Metaanalysis MetaAnalysis Journal Article Discrepancies between meta-analysis and subsequent large randomized, controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9262498 MetaAnalysis .   No .
Metaanalysis MetaAnalysis;Problem Journal Article Expressing the magnitude of adverse effects in case-control studies: "the number of patients needed to be treated for one additional patient to be harmed". https://www.ncbi.nlm.nih.gov/pubmed/?term=10678870 MetaAnalysis: Problem .   No .
Metaanalysis MetaAnalysis;Problem Journal Article Has the use of meta-analysis enhanced our understanding of terapies for postoperative nausea and vomiting? https://www.ncbi.nlm.nih.gov/pubmed/?term=10357318 MetaAnalysis: Problem .   No .
Metaanalysis MetaAnalysis;Preplanned Journal Article Hospital Outcomes Project for the Elderly (HOPE): Rationale and Design for a Prospective Poooled Analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=8440849 MetaAnalysis Pre-planned .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Invited Commentary: A Critical Look at Some Popular Meta-analytic methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8030632 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodological Issues Journal Article Lessons from overviews of cardiovascular trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3616285 MetaAnalysis Methodological Issues .   No .
Metaanalysis MetaAnalysis;Preplanned Journal Article Lessons learned from a prospective meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=7706636 MetaAnalysis Pre-planned .   No .
Metaanalysis MetaAnalysis;Problem Journal Article Measuring inconsistency in meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=12958120 MetaAnalysis: Problem .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Meta-analysis in lcinical trials reporting: has a tool become a weapon. https://www.ncbi.nlm.nih.gov/pubmed/?term=1536118 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Meta-analysis in epidemiology, with special reference to studies of the association between exposure to environmental tobacco smoke and lung cancer: a critique. https://www.ncbi.nlm.nih.gov/pubmed/?term=1995774 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Meta-analysis/Shmeta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=7977286 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Meta-analysis: The quantitative approach to research review. https://www.ncbi.nlm.nih.gov/pubmed/?term=3051403 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article Meta-meta-analysis: Unanswered questions about aggregating data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1995771 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Methodological Issues Journal Article Methods for pooled analyses of epidemiologic studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=8347739 MetaAnalysis Methodological Issues .   No .
Metaanalysis MetaAnalysis Journal Article Problems induced by meta-analyses. https://www.ncbi.nlm.nih.gov/pubmed/?term=1876787 MetaAnalysis .   No .
Metaanalysis MetaAnalysis;Problem Journal Article Spurious precision? Meta-analysis of observational studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=9462324 MetaAnalysis: Problem .   No .
Metaanalysis MetaAnalysis;Methodology Journal Article The J-Curve phenomenon and the treatment of hypertension. https://www.ncbi.nlm.nih.gov/pubmed/?term=1824642 MetaAnalysis Methodology .   No .
Metaanalysis MetaAnalysis;Specific Analysis Not Cardiology Journal Article Thiazide diuretic agents and the incidence of hip fracture. https://www.ncbi.nlm.nih.gov/pubmed/?term=2296269 MetaAnalysis-Specific Analysis Not Cardiology .   No .
Metaanalysis MetaAnalysis Journal Article Time to stop counting the tablets? https://www.ncbi.nlm.nih.gov/pubmed/?term=2758726 MetaAnalysis .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article Approaches to heterogeneity in meta-analysis https://www.ncbi.nlm.nih.gov/pubmed/?term=11746342 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article Meta-analyses of randomized controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3807986 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article Meta-analysis in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3802833 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article Randome-Effects Meta-analyses are not always conservative. https://www.ncbi.nlm.nih.gov/pubmed/?term=10472946 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article Randome-Effects Meta-analyses are not always conservative. https://www.ncbi.nlm.nih.gov/pubmed/?term=10472946 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodological Issues MetaAnalysis Methodological Issues Journal Article The effects of exercise on falls in elderly patients. A preplanned meta-analysis of the FICSIT Trials. Frailty and Injuries: Cooperative Studies of Intervention Techniques. https://www.ncbi.nlm.nih.gov/pubmed/?term=7715058 MetaAnalysis Methodological Issues .   No .
Metaanalysis Methodology MetaAnalysis Methodology Journal Article Comparison of MEDLINE Searching with a perinatal Trials database. https://www.ncbi.nlm.nih.gov/pubmed/?term=3907973 MetaAnalysis Methodology .   No .
Metaanalysis Methodology MetaAnalysis Methodology Journal Article Meta-analysis: reconciling the results of independent studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=7792440 MetaAnalysis Methodology .   No .
Method survival data analysis;competing risks;Kaplan-Meier;method; Journal Article A note on competing risks in survival data analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=15305188 Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events. .   No .
Methodological Issues MetaAnalysis; Methodological Issues Journal Article Detecting and describing heterogeneity in meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=9595615 MetaAnalysis Methodological Issues .   No .
Methodological Issues MetaAnalysis;Methodological Issues Journal Article Lessons from overviews of cardiovascular trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3616285 MetaAnalysis Methodological Issues .   No .
Methodological Issues MetaAnalysis;Methodological Issues Journal Article Methods for pooled analyses of epidemiologic studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=8347739 MetaAnalysis Methodological Issues .   No .
Methodology methodology;randomized control trials;quantitative method; Journal Article A quality assessment of randomized control trials of primary treatment of breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=3711962 The methodology of randomized control trials (RCTs) of the primary treatment of early breast cancer has been reviewed using a quantitative method. Sixty-three RCTs comparing various treatment modalities tested on over 34,000 patients and reported in 119 papers were evaluated according to a standardized scoring system. A percentage score was developed to assess the internal validity of a study (referring to the quality of its design and execution) and its external validity (referring to presentation of information required to determine its generalizability). An overall score was also calculated as the combination of the two. The mean overall score for the 63 RCTs was 50% (95% confidence interval [CI] = 46% to 54%) with small and nonstatistically significant differences between types of trial. The most common methodologic deficiencies encountered in these studies were related to the randomization process (only 27 of the 63 RCTs adopted a truly blinded procedure), the handling of withdrawals (only 26 RCTs included all patients in the analyses), the description of the follow-up schedule (only 12 RCTs reported adequately), the report of side effects (adequate information given in 33 RCTs), and the description of the patient population (satisfactory in 29 RCTs). Telephone calls to the principal investigators improved the quality scores by seven points on a scale of 100, indicating that some of the deficiencies lay in reporting rather than performance. There was evidence that quality has improved over time and that the increasing tendency of involving a biostatistician in the research team was positively associated with the improvement of the internal validity but not with the external. .   No .
Methodology randomized controlled trials;methodology Journal Article Assessing the quality of randomized controlled trials: an annotated bibliography of scales and checklists. https://www.ncbi.nlm.nih.gov/pubmed/?term=7743790 Assessing the quality of randomized controlled trials (RCTs) is important and relatively new. Quality gives us an estimate of the likelihood that the results are a valid estimate of the truth. We present an annotated bibliography of scales and checklists developed to assess quality. Twenty-five scales and nine checklists have been developed to assess quality. The checklists are most useful in providing investigators with guidelines as to what information should be included in reporting RCTs. The scales give readers a quantitative index of the likelihood that the reported methodology and results are free of bias. There are several shortcomings with these scales. Future scale development is likely to be most beneficial if questions common to all trials are assessed, if the scale is easy to use, and if it is developed with sufficient rigor. .   No .
Methodology randomized clinical trials;bias;methodology;analysis Journal Article Intention-to-treat analysis in randomized trials: who gets counted? https://www.ncbi.nlm.nih.gov/pubmed/?term=9378838 This article discusses the rationale and implications associated with the selection and use of analysis strategies for randomized clinical trials as they relate to protocol deviations. The topics addressed specifically are the conceptual and methodologic approaches and biases of clinical efficacy and effectiveness assessment. The authors suggest that different analytic strategies may be more or less appropriate depending on the intended audience. .   No .
Methodology methodology; Journal Article Methodological and statistical problems in sleep apnea research: the literature on uvulopalatopharyngoplasty. https://www.ncbi.nlm.nih.gov/pubmed/?term=8560132 A comprehensive review of the literature on the surgical treatment of sleep apnea found 37 appropriate papers (total n = 992) on uvulopalatopharyngoplasty (UPPP). Methodological and statistical problems in these papers included the following: 1) There were no randomized studies and few (n = 4) with control groups. 2) Median sample size was only 21.5; thus statistical power was low and clinically important associations were routinely classified as "not statistically significant". 3) Only one paper presented the confidence bounds that might distinguish between statistical and clinical significance. 4) Because of short follow-up time and infrequent repeat follow-ups, little is known about whether UPPP results deteriorate with time. 5) In at least 15 papers, bias caused by retrospective designs and nonrandom loss to follow-up raised questions about the generalizability of results. 6) Few papers associated polysomnographic data with patient-based quality of life measures. 7) Missing data and missing and inconsistent definitions were common. 8) Baseline measures were often biased because the same assessment was inappropriately but routinely used for both screening and baseline. We conclude that because of these and other problems, there is much that is needlessly unknown about UPPP. It is the responsibility of the research and professional communities to define training, editorial and review procedures that will raise the methodological and statistical quality of published research. .   No .
Methodology methodology;bias;randomized trials;design Journal Article Methodological bias in cluster randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=15743523 Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials. Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants. .   No .
Methodology methodology;bias;meta-analysis Journal Article Methodology and overt and hidden bias in reports of 196 double-blind trials of nonsteroidal antiinflammatory drugs in rheumatoid arthritis. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702836%5Buid%5D Important design aspects were decreasingly reported in NSAID trials over the years, whereas the quality of statistical analysis improved. In half of the trials, the effect variables in the methods and results sections were not the same, and the interpretation of the erythrocyte sedimentation rate in the reports seemed to depend on whether a significant difference was found. Statistically significant results appeared in 93 reports (47%). In 73 trials they favored only the new drug, and in 8 only the active control. All 39 trials with a significant difference in side effects favored the new drug. Choice of dose, multiple comparisons, wrong calculation, subgroup and within-groups analyses, wrong sampling units (in 63% of trials for effect variables, in 23% for side effects), change in measurement scale before analysis, baseline difference, and selective reporting of significant results were some of the verified or possible causes for the large proportion of results that favored the new drug. Doubtful or invalid statements were found in 76% of the conclusions or abstracts. Bias consistently favored the new drug in 81 trials, and the control in only one trial. It is not obvious how a reliable meta-analysis could be done in these trials. .   No .
Methodology compliance;methodology Journal Article Methods in assessing drug compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=6588737 Methods and problems in assessing drug compliance are related to the selection of study sample and observation period as well as to methods used to measure patient behaviour in taking medications. Patients under treatment with a certain drug regimen are different from the patients for whom that regimen was originally prescribed. Medication compliance during short periods of time, such as 1-2 weeks before or after a visit to the clinic, is likely to be different from that found over longer periods of time. Several studies indicate that estimates by clinical staff are no more accurate than chance selections in determining medication compliance. Patient interviews have usually identified 25-50% of noncompliant patients, but interview data on spacing between doses seem to be more accurate. Pill counts are useful in assessing drug compliance, although compliance may sometimes be overestimated. Medication monitors provide more detailed information on patient behaviour in taking medications. .   No .
Methodology compliance;methodology;interpreting results Journal Article Patient compliance and medical research: issues in methodology. https://www.ncbi.nlm.nih.gov/pubmed/?term=8164085 Compliance with medication and medical appointments is presumed to have a critical influence on outcomes of medical interventions. Readers of the medical literature should consider how compliance was measured and analyzed when interpreting the results of clinical trials. Table 4 suggests criteria for critical appraisal of compliance-related issues in reports of clinical trials. .   No .
Methodology MetaAnalysis;Methodology Journal Article Invited Commentary: A Critical Look at Some Popular Meta-analytic methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8030632 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article Meta-analysis in lcinical trials reporting: has a tool become a weapon. https://www.ncbi.nlm.nih.gov/pubmed/?term=1536118 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article Meta-analysis in epidemiology, with special reference to studies of the association between exposure to environmental tobacco smoke and lung cancer: a critique. https://www.ncbi.nlm.nih.gov/pubmed/?term=1995774 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article Meta-analysis/Shmeta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=7977286 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article Meta-analysis: The quantitative approach to research review. https://www.ncbi.nlm.nih.gov/pubmed/?term=3051403 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article Meta-meta-analysis: Unanswered questions about aggregating data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1995771 MetaAnalysis Methodology .   No .
Methodology MetaAnalysis;Methodology Journal Article The J-Curve phenomenon and the treatment of hypertension. https://www.ncbi.nlm.nih.gov/pubmed/?term=1824642 MetaAnalysis Methodology .   No .
Methods methods;randomized trials;analysis; Journal Article A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8500308 The standard approach to analyzing randomized trials ignores information on postrandomization compliance. Application of these methods results in estimates that may lack the desired causal interpretation. We employ a new method of estimation and analyze data from the Multiple Risk Factor Intervention Trial (MRFIT) to estimate the causal effect of quitting cigarette smoking. Our procedure utilizes a method proposed by Robins and Tsiatis and allows us to take advantage of postrandomization smoking history without requiring untenable assumptions about the comparability of compliers and noncompliers. We contrast the performance of our method and the standard intent-to-treat analysis in the MRFIT data and in simulated data in which compliance rates are varied. .   No .
Methods methods;analysis;clinical trial;randomization; Journal Article Adjusting for non-compliance and contamination in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9160496 A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive. .   No .
Methods analysis;regression models;methods Journal Article An annotated bibliography of methods for analysing correlated categorical data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1557577 This paper provides an annotated bibliography of over 100 articles concerning methods for analysing correlated categorical response data. Most of the papers listed here concern categorical regression models and estimation, with particular emphasis on binary responses. The papers are classified by several characteristics which group them according to common themes. The bibliography serves as a reference of methods for analysts of correlated categorical data, as well as for persons interested in methodologic work in this active area of statistical research. .   No .
Methods design;analysis;group-randomized trials; methods Journal Article Design and analysis of group-randomized trials: a review of recent practices. https://www.ncbi.nlm.nih.gov/pubmed/?term=14998802 We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published. .   No .
Methods methods Journal Article Methods to reduce the impact of intraclass correlation in group-randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=12568061 This study reports intraclass correlation (ICC) for dependent variables used in group-randomized trials (GRTs). The authors also document the effect of two methods suggested to reduce the impact of ICC in GRTs; these two methods are modeling time and regression adjustment for covariates. They coded and analyzed 1,188 ICC estimates from 17 published, in press, and unpublished articles representing 21 studies. Findings confirm that both methods can improve the efficiency of analyses shown to be valid across conditions common in GRTs. Investigators planning GRTs should obtain ICC estimates matched to their planned analysis so that they can size their studies properly. .   No .
Methods Propensity scores;methods Journal Article Propensity score methods. https://www.ncbi.nlm.nih.gov/pubmed/20103037 Propensity scores .   No .
Methods nonparometric;statistical;methods Journal Article On the efficacy of the rank transformation in stepwise logistic and discriminant analysis. https://www.ncbi.nlm.nih.gov/pubmed/8446809 Nonparometric Statistical Methods .   No .
Methods non parametric;statistical;methods Journal Article Should we always choose a nonparametric test when comparing two apparently nonnormal distributions? https://www.ncbi.nlm.nih.gov/pubmed/?term=11165471 Non parametric Statistical Methods .   No .
Misclassification misclassification Journal Article Bias due to misclassification in the estimation of relative risk. https://www.ncbi.nlm.nih.gov/pubmed/?term=871121 Misclassification .   No .
Misclassification Misclassification Journal Article On the distortion of risk estimates in multiple exposure level case-control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=7211828 Misclassification .   No .
Misclassification misclassification Journal Article Can dropout and other noncompliance be minimized in a clinical trial? Report from the Veterans Administrative National Heart, Lung and Blood Institute cooperative study on antihypertensive therapy: mild hypertension. https://www.ncbi.nlm.nih.gov/pubmed/?term=6749426 Misclassification .   No .
Misclassification misclassification Journal Article Crossovers in coronary artery bypass grafting trials: desirable, undesirable, or both? https://www.ncbi.nlm.nih.gov/pubmed/?term=2679461 Misclassification .   No .
Misclassification misclassification Journal Article Estimating and correcting for confounder misclassification. https://www.ncbi.nlm.nih.gov/pubmed/?term=2705426 Misclassification .   No .
Misclassification misclassification Journal Article THE DILUTION EFFECT OF MISCLASSIFICATION. https://www.ncbi.nlm.nih.gov/pubmed/?term=14136324 Misclassification .   No .
Misclassification misclassification Journal Article Bias due to misclassification in the estimation of relative risk. https://www.ncbi.nlm.nih.gov/pubmed/?term=871121 Misclassification .   No .
Misclassification misclassification Journal Article The problem of attributing deaths of nonadherers: the VA coronary bypass experience. https://www.ncbi.nlm.nih.gov/pubmed/?term=6761063 Misclassificaiton .   No .
Misclassification misclassification Journal Article Misclassification of a prognostic dichotomous variable: sample size and parameter estimate adjustment. https://www.ncbi.nlm.nih.gov/pubmed/?term=7569489 Misclassification .   No .
Misclassification misclassification Journal Article When will nondifferential misclassification of an exposure preserve the direction of a trend? https://www.ncbi.nlm.nih.gov/pubmed/?term=8067350 Misclassification .   No .
Misclassification misclassification Journal Article Bias in relative odds estimation owing to imprecise measurement of correlated exposures. https://www.ncbi.nlm.nih.gov/pubmed/?term=1604073 Misclassification .   No .
Misclassification misclassification Journal Article Implications of measurement error in exposure for the sample sizes of case-control studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=8109576 Misclassification .   No .
Misclassification misclassification Journal Article Use of the positive predictive value to correct for disease misclassification in epidemiologic studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=8256775 Misclassification .   No .
Misclassification misclassification Journal Article Inferences on the potential effects of presumed nondifferential exposure misclassification. https://www.ncbi.nlm.nih.gov/pubmed/?term=8275202 Misclassification .   No .
Misclassification misclassification Journal Article The effects of data entry error: an analysis of partial verification. https://www.ncbi.nlm.nih.gov/pubmed/?term=2350961 Misclassification .   No .
Misclassification misclassification Journal Article Effect of crossover on the statistical power of randomized studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=2802849 Misclassification .   No .
Misconduct clinical trials; misconduct Journal Article Problems in clinical trials go far beyond misconduct. https://www.ncbi.nlm.nih.gov/pubmed/?term=8202708 Problems in clinical trials go far beyond misconduct. .   No .
Missing Data missing data Book Missing Data: Analysis and Design https://amazon.com/dp/1461440173 Missing Data: Analysis and Design contains essential information for both beginners and advanced readers. For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems. For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. 4 SAS, SPSS Yes 3
Missing Data Missing Data Journal Article Missing covariate data in medical research: to impute is better than to ignore. https://www.ncbi.nlm.nih.gov/pubmed/?term=20338724 Missing data .   No .
Missing Data Missing data;multiple-model;multiple imputation;clinical trial Journal Article Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=23503984 Missing Data .   No .
Missing Data Missing data Journal Article Missing data: what a little can do, and what researchers can do in response. https://www.ncbi.nlm.nih.gov/pubmed/?term=19932803 Missing data .   No .
Mixed Effect Models regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Mixed Models mixed models; repeated measures data; cross-over trials, multi-center trials Book applied mixed models in medicine https://www.amazon.com/Applied-Models-Medicine-Statistics-Practice/dp/1118778251 very useful reference for analysis of longitudinal and correlated data using mixed models. good examples, SAS codes provided 4   No 4
Mode distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Model regression; model Journal Article A two-compartment regression model applied to compliance in a hypertension treatment program. https://www.ncbi.nlm.nih.gov/pubmed/?term=7410524 A two-compartment regression model applied to compliance in a hypertension treatment program. .   No .
Model Adequacy power analysis; bias (epidemiology); model adequacy; type I error; cox proportional hazards models; logistic regression. Journal Article Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression https://academic.oup.com/aje/article/165/6/710/63906 Commentary and simulation about back-of-the-envelope power analyses for logistic and Cox regression. 2 Any No 1
Modeling Linear Regression; Modeling Online Interactive Course Linear Regression and Modeling https://www.coursera.org/learn/linear-regression-model Per the course website, this course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio. . Any Yes .
Momma Distributions power;sample size;momma distributions Journal Article Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events. ACIP Investigators. Asymptomatic Cardiac Ischemia Pilot. https://www.ncbi.nlm.nih.gov/pubmed/?term=8047740 Power and sample size; Counts of events/Gomma Distributions .   No .
Monitoring Clinical Trials;Ethics;Fraud;Monitoring Journal Article The dangers of inferring treatment effects from observational data: a case study in HIV infection https://www.ncbi.nlm.nih.gov/pubmed/?term=11943438 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Monitoring Clinical Trials;Ethics;Fraud;Monitoring Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Monitoring Clinical Trials;Ethics;Fraud;Monitoring Journal Article When Was a 'Negative' Clinical Trail Big Enough? How Many Patients You Needed Depends on What You Found. https://www.ncbi.nlm.nih.gov/pubmed/?term=3985731 Clinical Trials: Ethics, Fraud, Monitoring .   No .
Multicenter Trials multicenter trials;analysis;data Journal Article Guidelines for quality assurance in multicenter trials: a position paper. https://www.ncbi.nlm.nih.gov/pubmed/?term=9741868 Multicenter trials: Analysis of Data .   No .
Multicenter Trials multicenter trials;analysis;data Journal Article Publications from multicentre clinical trials: statistical techniques and accessibility to the reader. https://www.ncbi.nlm.nih.gov/pubmed/?term=7701142 Multicenter Trials: analysis of Data .   No .
Multicenter Trials multicenter trials;analysis;data Journal Article Evaluation of multicentre clinical trial data using adaptations of the Mosteller-Tukey procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341865 Multicenter Trials: Analysis of Data .   No .
Multicenter Trials multicenter trials;analysis;data Journal Article Tests for qualitative treatment-by-centre interaction using a 'pushback' procedure. https://www.ncbi.nlm.nih.gov/pubmed/?term=8341864 Multicenter trials: Analysis of Data .   No .
Multilevel multilevel;hierarchical;regress;data Journal Article Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures. https://www.ncbi.nlm.nih.gov/pubmed/?term=20949128 Multilevel/Hierarchical Regress .   No .
Multilevel multilevel;hierarchical;regress;data Journal Article Tutorial in biostatistics. An introduction to hierarchical linear modelling. https://www.ncbi.nlm.nih.gov/pubmed/?term=10327531 Multilevel / Hierarchical Regress .   No .
Multilevel multilevel;hierarchical;regress;data Journal Article Multi-level analysis in epidemiologic research on health behaviors and outcomes. https://www.ncbi.nlm.nih.gov/pubmed/1632420 Multilevel / Hierarchical Regress .   No .
Multilevel Analysis multilevel analysis; hierarchical linear model; random coefficients models; intra-class correlation coefficient; Book Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling https://www.amazon.com/Multilevel-Analysis-Introduction-Advanced-Modeling/dp/184920201X/ref=mt_paperback?_encoding=UTF8&me= Introduction to multilevel analysis, topics include hierarchical linear model, random coefficients models, how to calculate ICC, how much does a model explain, etc. 5 SAS, SPSS, R, M+, Yes 4
Multilevel Linear Models repeated measures; multilevel linear models;repeated measures data; Journal Article Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. https://www.ncbi.nlm.nih.gov/pubmed/?term=23148474 Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression. .   No .
Multiple multiple;comparisons;data;analysis Journal Article Gatekeeping Strategies for Avoiding False-Positive Results in Clinical Trials With Many Comparisons. https://www.ncbi.nlm.nih.gov/pubmed/?term=29049572 Multiple Comparisons .   No .
Multiple multiple;comparisons;data;analysis Journal Article P-value interpretation and alpha allocation in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/9708870 Multiple Comparisons .   No .
Multiple multiple;comparisons;data;analysis Journal Article Multiple comparisons and related issues in the interpretation of epidemiologic data https://www.ncbi.nlm.nih.gov/pubmed/?term=7572970 Multiple Comparisons .   No .
Multiple multiple;comparisons;data;analysis Journal Article Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=8629727 Multiple Comparisons .   No .
Multiple multiple;comparisons;data;analysis Journal Article Impact of multiple comparisons in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3661589 Multiple Comparisons .   No .
Multiple multiple;comparisons;data;analysis Journal Article Multiple hypothesis tests in multiple investigations. https://www.ncbi.nlm.nih.gov/pubmed/?term=7792449 Multiple Comparisons .   No .
Multiple Comparisons biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Multiple Comparisons one way analysis of variance; multiple comparisons; orthogonal contrasts Journal Article Statistics review 9: One-way analysis of variance https://www.ncbi.nlm.nih.gov/pubmed/15025774 This review introduces one-way analysis of variance, which is a method of testing differences between more than two groups or treatments. Multiple comparison procedures and orthogonal contrasts are described as methods for identifying specific differences between pairs of treatments. 2   No .
Multiple Comparisons multiple comparisons;data;analysis Journal Article Invited commentary: Re: "Multiple comparisons and related issues in the interpretation of epidemiologic data". https://www.ncbi.nlm.nih.gov/pubmed/?term=9583708 Multiple Comparisons .   No .
Multiple Imputation Missing data;multiple-model;multiple imputation;clinical trial Journal Article Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=23503984 Missing Data .   No .
Multiple Regression biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Multiple-Model Missing data;multiple-model;multiple imputation;clinical trial Journal Article Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=23503984 Missing Data .   No .
Multivariate power; sample size; calculator; multivariate Interactive Program GLIMPPSE online Power and Sample Size Calculation http://glimmpse.samplesizeshop.org Well-documented online power calculator with guided steps. "GLIMMPSE can compute power or sample size for univariate and multivariate linear models with Gaussian errors." 59-page user manual is available at http://samplesizeshop.org/files/2012/08/GLIMMPSEUserManual_v2.0.0.pdf. 4   No 1
Multivariate longitudinal data analysis;multivariate Journal Article Estimating incidence and diagnostic error rates for bivariate progressive processes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8117898 Longitudinal Data Analysis:Multivariate .   No .
Multivariate longitudinal data analysis;multivariate Journal Article A multivariate growth curve model for pregnancy. https://www.ncbi.nlm.nih.gov/pubmed/1912265 Longitudinal Data Analysis: Multivariate .   No .
Multivariate longitudinal data analysis;multivariate Journal Article Multivariate methods for clustered ordinal data with applications to survival analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=9044526 Longitudinal Data Analysis;multivariate .   No .
Multivariate Analysis Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Multivariate Analysis Linear Models; Multivariate Analysis; Regression Analysis Video Instruction Introduction to Model Selection https://www.youtube.com/watch?v=VB1qSwoF-l0&list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I This 5-minute video provides an overview to use of forward selection and backward elimination in developing a multivariable linear regression model. 2   No 1
Multivariate Analysis regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Network Analysis network analysis; factor analysis Website Psych Networks http://psych-networks.com/ Rich website for the use of network analysis, particularly for analyses in psychology by one of the chief proponents of the technique. In many aspects, the analyses are similar to those of factor analysis, but with differing underlying assumptions. Longitudinal models are included. 4 R No 4
Network Analysis network analysis; psychopathology; latent variable models; psychometrics Journal Article Network analysis: an integrative approach to the structure of psychopathology. https://www.ncbi.nlm.nih.gov/pubmed/23537483 In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry -> insomnia -> fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions. 3   No 1
Non Parametric non parametric;statistical;methods Journal Article Should we always choose a nonparametric test when comparing two apparently nonnormal distributions? https://www.ncbi.nlm.nih.gov/pubmed/?term=11165471 Non parametric Statistical Methods .   No .
Noncompliance noncompliance;compliance Journal Article Detection methods and strategies for improving medication compliance. https://www.ncbi.nlm.nih.gov/pubmed/?term=1928147 The reliability of compliance detection methods and practical strategies for improving patient compliance with drug therapy are reviewed. Detection of noncompliance is a necessary prerequisite for adequate treatment. Noncompliance can be detected by indirect methods (e.g., self-report, interview, therapeutic outcome, pill count, computerized compliance monitors) or direct methods (e.g., biologic markers, tracer compounds, biologic assay of body fluids). In general, the direct methods of detection have a higher sensitivity and specificity than the indirect methods. Computerized compliance monitors are the most recent and reliable of the indirect-detection methods. Strategies for improving compliance involve identification of risk factors for non-compliance; development, with the patient's participation, of an individualized treatment plan that simplifies the regimen as much as possible; education of the patient, including information about his or her illness, instructions on how to take the prescribed medication correctly, and an explanation of the benefits and possible adverse effects of the therapy; and, if necessary, use of compliance aids such as medication calendars, special containers, caps, and dispensing systems, or compliance packaging. The patient should be taught to monitor his or her own treatment regimen. Follow-up monitoring by health-care professionals, including pharmacists, will also help ensure that the patient is complying with the treatment regimen. Health-care practitioners need to understand factors that contribute to noncompliance and to use effective methods for assessing and monitoring compliance in conjunction with strategies aimed at increasing compliant behavior. .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. https://www.ncbi.nlm.nih.gov/pubmed/16522836 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Lessons from and cautions about noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16522840 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Significance testing to establish equivalence between treatments, with special reference to data in the form of 2X2 tables. https://www.ncbi.nlm.nih.gov/pubmed/?term=588654 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Bioequivalence revisited. https://www.ncbi.nlm.nih.gov/pubmed/?term=1485060 Nonin priority/Equivalence trials - Bioequivalence. .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article Conventional null hypothesis testing in active control equivalence studies. https://www.ncbi.nlm.nih.gov/pubmed/8582153 Nonin priority/Equivalence Trials - Bioequivalence .   No .
Nonin nonin;priority;equivalence;trials;bioequivalence Journal Article A comparison of continuous infusion of alteplase with double-bolus administration for acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/9340504 Nonin prioritiy/Equivalence Trials - bioequivalence .   No .
Nonin nonin;priority;equivalence;trials Journal Article Good enough: a primer on the analysis and interpretation of noninferiority trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16818930 Nonin Priority/Equivalence Trials .   No .
Nonin nonin;priority;equivalence;trials Journal Article Alternatives to Hazard Ratios for Comparing the Efficacy or Safety of Therapies in Noninferiority Studies. https://www.ncbi.nlm.nih.gov/pubmed/26054047 Nonin Priority/Equivalence Trials .   No .
Nonin nonin;priority;equivalence;trials Journal Article Non-inferiority trials: design concepts and issues - the encounters of academic consultants in statistics. https://www.ncbi.nlm.nih.gov/pubmed/?term=12520555 Nonin Priority/Equivalence Trials .   No .
Nonin nonin;priority;equivalence;trials Journal Article Implementation of an adaptive group sequential design in a bioequivalence study. https://www.ncbi.nlm.nih.gov/pubmed/?term=17436336 Nonin Priority/Equivalence Trials .   No .
Nonin nonin;priority;equivalence;trials Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority/Equivalence Trials .   No .
Noninferiority Trials Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Nonparametric Kruskal-Wallis; Jonckheere-Terpstra; Friendman; nonparametric Journal Article Statistics review 10: Further nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/15153238 A previous review described analysis of variance, the method used to test for differences between more than two groups or treatments. However, in order to use analysis of variance, the observations are assumed to have been selected from Normally distributed populations with equal variance. The tests described in this review require only limited assumptions about the data. The Kruskal-Wallis test is the nonparametric alternative to one-way analysis of variance, which is used to test for differences between more than two populations when the samples are independent. The Jonckheere-Terpstra test is a variation that can be used when the treatments are ordered. When the samples are related, the Friedman test can be used. 2   No 2
Nonparametric Tests sign test; Wilcoxon signed rank test; Wilcoxon rank sum test; Mann-Whitney test; nonparametric tests Journal Article Statistics review 6: Nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/12493072 The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed. 2   No .
Nonparametric Tests sample size; nonparametric tests Journal Article Sample Size Determination for Some Common Nonparametric Tests http://links.jstor.org/sici?sici=0162-1459%28198706%2982%3A398%3C645%3ASSDFSC%3E2.0.CO%3B2-V sample size calculation for common Nonparametric Tests like sign test, Wilcoxon one-sample and two-sample tests, Kendall's test. 4 Any No 4
Nonparometric nonparometric;statistical;methods Journal Article On the efficacy of the rank transformation in stepwise logistic and discriminant analysis. https://www.ncbi.nlm.nih.gov/pubmed/8446809 Nonparometric Statistical Methods .   No .
Nonrandomized randomized; nonrandomized; treatment effects Journal Article Comparison of evidence of treatment effects in randomized and nonrandomized studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=11497536 randomized and nonrandomized studies .   No .
Nosql SQL; NoSQL Online Interactive Course Data Science at Scale Specialization https://www.coursera.org/specializations/data-science Per the course website, this 4-day course prepared by the University of Washington covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you . Any Yes .
Observational Data observational data; propensity score methods; cost-effectiveness analysis Book Analysis of Observational Health Care Data Using SAS https://www.amazon.com/Analysis-Observational-Health-Care-Using/dp/1607642271/ref=sr_1_1?s=books&ie=UTF8&qid=1515099599&sr=1-1&keywords=analysis+of+observational+health+care+data+using+sas analysis of Observational Health Care Data Using SAS with many SAS code examples, includes examples of propensity score methods and cost-effectiveness analysis, etc. very practical and useful. 4 SAS Yes 4
Observational Data Observational Data Online Interactive Course A Crash Course in Causality: Inferring Causal Effects from Observational Data https://www.coursera.org/learn/crash-course-in-causality Per the course website, this course prepared by the University of Pennsylvania provide the following: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method . Any No .
Observational Data observational data; effect sizes; large data Journal Article Tips for Analyzing Large Data Sets From the JAMA Surgery Statistical Editors https://www.ncbi.nlm.nih.gov/pubmed/29617520 With the advent of administrative databases and patient registries, big data is increasingly accessible to researchers. The large sample size of these data sets make the study of rare outcomes easier and provide the potential to determine national estimates and regional variations. As such, the JAMA Surgery editors and reviewers have seen more submissions using big data to answer clinical and policy-related questions. However, no database is completely free of bias and measurement error. With bigger data, random signals may denote statistical significance, and precision may be incorrectly inferred because of narrow confidence intervals. While many principles apply to all studies, the importance of these methodological issues is amplified in large, complex data sets. 1 Any No 1
Observational Data causal mediation; mediator; observational data; counterfactual analysis; Website Causal Mediation Analysis with the CAUSALMED Procedure https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1991-2018.pdf Important policy and health care decisions often depend on understanding the direct and indirect (mediated) effects of a treatment on an outcome. For example, does a youth program directly reduce juvenile delinquent behavior, or does it indirectly reduce delinquent behavior by changing the moral and social values of teenagers? Or, for example, is a particular gene directly responsible for causing lung cancer, or does it have an indirect (mediated) effect through its influence on smoking behavior? Causal mediation analysis deals with the mechanisms of causal treatment effects, and it estimates direct and indirect effects. A treatment variable is assumed to have causal effects on an outcome variable through two pathways: a direct pathway and a mediated (indirect) pathway through a mediator variable. This paper introduces the CAUSALMED procedure, new in SAS/STAT® 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework. The paper also defines these causal mediation and related effects in terms of counterfactual outcomes and describes the assumptions that are required for unbiased estimation. Examples illustrate the ideas behind causal mediation analysis and the applications of the CAUSALMED procedure. 4 SAS No 3
Observational Study propensity score; observational study Journal Article Propensity score matching: a conceptual review for radiology researchers https://www.ncbi.nlm.nih.gov/pubmed/25741190 The purpose of this article is to provide a step-by-step nonmathematical conceptual guide to propensity score analysis with particular emphasis on propensity score matching. 2   No 2
Odds Ratio relative risk; odds ratio; risk factor Journal Article Statistics Review 11: Assessing Risk https://www.ncbi.nlm.nih.gov/pubmed/15312212 Relative risk and odds ratio have been introduced in earlier reviews (see Statistics reviews 3, 6 and 8). This review describes the calculation and interpretation of their confidence intervals. The different circumstances in which the use of either the relative risk or odds ratio is appropriate and their relative merits are discussed. A method of measuring the impact of exposure to a risk factor is introduced. Measures of the success of a treatment using data from clinical trials are also considered. 2 Any No 2
One Compartment Model pharmacokinetic models; PK; one compartment model; two compartment model; three compartment model; Website Fitting Compartment Models Using PROC NLMIXED https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1883-2018.pdf The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure). 4 SAS No 4
One Way Analysis Of Variance one way analysis of variance; multiple comparisons; orthogonal contrasts Journal Article Statistics review 9: One-way analysis of variance https://www.ncbi.nlm.nih.gov/pubmed/15025774 This review introduces one-way analysis of variance, which is a method of testing differences between more than two groups or treatments. Multiple comparison procedures and orthogonal contrasts are described as methods for identifying specific differences between pairs of treatments. 2   No .
Online Library, Statistics, Programming, Technology online library, statistics, programming, technology Website Safari Books Online http://proquest.safaribooksonline.com/?uicode=washumo Online digital library of statistical and technical books. Available to all WUSTL personnel. Optional account allows user to save books to a personal library within Safari. 1 Any No 1
Ophthalmology Reliability;Ophthalmology Journal Article Reproducibility of visual acuity measurements in patients with retinitis pigmentosa. https://www.ncbi.nlm.nih.gov/pubmed/9051840 Reliability studies: Ophthalmology .   No .
Ophthalmology reliability; ophthalmology Journal Article On the statistical reliability of letter-chart visual acuity measurements. https://www.ncbi.nlm.nih.gov/pubmed/8425819 Reliability studies: Ophthalmology .   No .
Ophthalmology reliability; ophthalmology Journal Article Assessing the reliability, discriminative ability, and validity of disability glare tests. https://www.ncbi.nlm.nih.gov/pubmed/?term=8425818 Reliability studies: ophthalmology .   No .
Ophthalmology reliability; ophthalmology Journal Article Validity and reliability of visual acuity measurements. https://www.ncbi.nlm.nih.gov/pubmed/?term=3253626 Reliability studies: ophthalmology .   No .
Ophthalmology Reliability; ophthalmology Journal Article Measuring resolution in the contrast domain: the small letter contrast test. https://www.ncbi.nlm.nih.gov/pubmed/?term=8807651 Reliability studies: ophthalmology .   No .
Ophthalmology Reliability;Ophthalmology Journal Article Reproducibility of anthropometric and body composition measurements: the HERITAGE Family Study. https://www.ncbi.nlm.nih.gov/pubmed/?term=9130027 Reliability studies: Ophthalmology .   No .
Ophthalmology reliability studies;ophthalmology Journal Article Reliability of ophthalmic diagnoses in an epidemiologic survey. https://www.ncbi.nlm.nih.gov/pubmed/6881130 Reliability studies: ophthalmology .   No .
Ophthalmology Reliability;ophthalmology Journal Article Comparison of generic versus disease-specific measures of functional impairment in patients with cataract. https://www.ncbi.nlm.nih.gov/pubmed/?term=7723440 Reliability studies: ophthalmology .   No .
Ophthalmology Reliability;ophthalmology Journal Article Reproducibility of refraction and visual acuity measurement under a standard protocol. The Macular Photocoagulation Study Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=2480626 Reliability studies: ophthalmology .   No .
Ophthalmology Reliability;ophthalmology Journal Article Reproducibility and responsiveness of the VF-14. An index of functional impairment in patients with cataracts. https://www.ncbi.nlm.nih.gov/pubmed/7487617 Reliability studies; ophthalmology .   No .
Ophthalmology Reliability;Ophthalmology Journal Article The VF-14. An index of functional impairment in patients with cataract. https://www.ncbi.nlm.nih.gov/pubmed/8185520 Reliability studies: Ophthalmology .   No .
Ophthalmology Reliability;ophthalmology Journal Article Interobserver reliability of the teller acuity card procedure in pediatric patients. https://www.ncbi.nlm.nih.gov/pubmed/8550321 Reliability studies; ophthalmology .   No .
Ophthalmology Reliability;ophthalmology Journal Article The use of accurate visual acuity measurements in clinical anti-cataract formulation trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3253632 Reliability studies: ophthalmology .   No .
Ordered ordered;alternatives;data;analysis Journal Article Analyzing data from ordered categories. https://www.ncbi.nlm.nih.gov/pubmed/6749191 Ordered Alternatives .   No .
Ordered ordered;alternatives;data;analysis Journal Article Standards for the use of ordinal scales in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3080061 Ordered Alternatives .   No .
Ordered ordered;alternatives;data;analysis Journal Article Ordinal scale and statistics in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3081161 Ordered Alternatives .   No .
Ordered power sample size;ordered;categorical data Journal Article Sample size calculations for paired or matched ordinal data. https://www.ncbi.nlm.nih.gov/pubmed/9699235 Power Sample Size: Ordered Categorical Data .   No .
Ordered power;sample size;ordered;categorical;data Journal Article Sample size calculations for ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/8134732 Power/sample size: ordered categorical data .   No .
Ordered Alternatives ordered alternatives Journal Article Regression with an ordered categorical response. https://www.ncbi.nlm.nih.gov/pubmed/2772438 Ordered Alternatives .   No .
Ordinal Multinomial Logistic logistic regression;ordinal multinomial logistic Other Multinomial and ordinal logistic regression using PROC LOGISTIC https://www.scribd.com/document/199770869/Multinomial-and-ordinal-logistic-regression-using-PROC-LOGISTIC Logistic Regression .   No .
Orthogonal Contrasts one way analysis of variance; multiple comparisons; orthogonal contrasts Journal Article Statistics review 9: One-way analysis of variance https://www.ncbi.nlm.nih.gov/pubmed/15025774 This review introduces one-way analysis of variance, which is a method of testing differences between more than two groups or treatments. Multiple comparison procedures and orthogonal contrasts are described as methods for identifying specific differences between pairs of treatments. 2   No .
Outbreak And Cluster Investigations outbreak and cluster investigations Book Applied Epidemiology: Theory to Practice http://beckercat.wustl.edu/cgi-bin/koha/opac-detail.pl?biblionumber=5344 Chapter 3: outbreak and cluster investigations. .   No .
Outliers distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
P Value hypothesis testing; significance; P value Journal Article Statistics review 3: Hypothesis testing and P values https://www.ncbi.nlm.nih.gov/pubmed/12133182 The present review introduces the general philosophy behind hypothesis (significance) testing and calculation of P values. Guidelines for the interpretation of P values are also provided in the context of a published example, along with some of the common pitfalls. Examples of specific statistical tests will be covered in future reviews. 2   No .
P Value clinical trials;bayesian;analysis;p value Journal Article Clinical trials and statistical verdicts: probable grounds for appeal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6830080 Conventional interpretation of clinical trials relies heavily on the classic p value. The p value, however, represents only a false-positive rate, and does not tell the probability that the investigator's hypothesis is correct, given his observations. This more relevant posterior probability can be quantified by an extension of Bayes' theorem to the analysis of statistical tests, in a manner similar to that already widely used for diagnostic tests. Reanalysis of several published clinical trials according to Bayes' theorem shows several important limitations of classic statistical analysis. Classic analysis is most misleading when the hypothesis in question is already unlikely to be true, when the baseline event rate is low, or when the observed differences are small. In such cases, false-positive and false-negative conclusions occur frequently, even when the study is large, when interpretation is based solely on the p value. These errors can be minimized if revised policies for analysis and reporting of clinical trials are adopted that overcome the known limitations of classic statistical theory with applicable bayesian conventions. .   No .
P Values p values;confidence intervals Journal Article On P values and confidence intervals (why can't we P with more confidence?) https://www.ncbi.nlm.nih.gov/pubmed/?term=8504558 On P values and confidence intervals .   No .
Paired paired;community;designs;sample;size Journal Article Breaking the matches in a paired t-test for community interventions when the number of pairs is small. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481187 Paired Community Designs Sample Size .   No .
Paired paired;community;designs;sample size Journal Article Planning for the appropriate analysis in school-based drug-use prevention studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=2212183 Paired Community Designs Sample Size .   No .
Paired paired;community;designs;sample;size Journal Article Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=2066748 Paired Community Designs Sample Size .   No .
Paired paired;community;design;sample size Journal Article Aspects of statistical design for the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/?term=1315664 Paired Community Design Sample Size .   No .
Paired paired;community;designs;sample size Journal Article A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. https://www.ncbi.nlm.nih.gov/pubmed/?term=2084005 Paired Community Design Sample Size .   No .
Paired Data data analysis;paired data Journal Article Methods to quantify the relation between disease progression in paired eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=10853635 Data Analysis:Paired data .   No .
Paired Data data analysis;paired data Journal Article Statistical methodology for paired cluster designs. https://www.ncbi.nlm.nih.gov/pubmed/3661544 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Accounting for the correlation between fellow eyes in regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/1543458 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Comparison of alternative regression models for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/?term=8073198 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Regression analysis for correlated data. https://www.ncbi.nlm.nih.gov/pubmed/8323597 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Maximum likelihood regression methods for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/2281239 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Maximum likelihood regression methods for paired binary data. https://www.ncbi.nlm.nih.gov/pubmed/2281239 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Analyzing correlated binary data using SAS. https://www.ncbi.nlm.nih.gov/pubmed/2350962 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Statistical methods in ophthalmology: an adjustment for the intraclass correlation between eyes. https://www.ncbi.nlm.nih.gov/pubmed/7082754 Data Analysis;Paired data .   No .
Paired Data data analysis;paired data Journal Article Multivariate methods in ophthalmology with application to other paired-data situations. https://www.ncbi.nlm.nih.gov/pubmed/?term=6534406 Data Analysis:Paired data .   No .
Paired Data data analysis;paired data Journal Article Two eyes or one? The data analyst's dilemma. https://www.ncbi.nlm.nih.gov/pubmed/3173980 Data Analysis;Paired data .   No .
Paired Data data analysis;paired data Journal Article Eyes or patients? Traps for the unwary in the statistical analysis of ophthalmological studies. https://www.ncbi.nlm.nih.gov/pubmed/3663557 Data Analysis: Paired data .   No .
Paired Data data analysis;paired data Journal Article Marginal models for correlated binary responses with multiple classes and multiple levels of nesting. https://www.ncbi.nlm.nih.gov/pubmed/?term=1420848 Data Analysis; Paired data .   No .
Paired Data data analysis;paired data Journal Article Statistical analysis of multi-eye data in ophthalmic research. https://www.ncbi.nlm.nih.gov/pubmed/?term=4019113 Data Analysis;Paired data .   No .
Paired Data data analysis;paired data Journal Article Appropriate statistical methods to account for similarities in binary outcomes between fellow eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8163336 Data Analysis:Paired data .   No .
Paired Data Survival Methods data analysis;paired data survival methods Journal Article A comparison of several tests for censored paired data. https://www.ncbi.nlm.nih.gov/pubmed/1579758 Data Analysis: paired data-survival methods .   No .
Paired Data Survival Methods data analysis;paired data survival methods Journal Article Modelling paired survival data with covariates. https://www.ncbi.nlm.nih.gov/pubmed/?term=2655727 Data Analysis: Paired data-survival methods .   No .
Paired Eyes longitudianl data analysis;paired eyes Journal Article Methods to quantify the relation between disease progression in paired eyes. https://www.ncbi.nlm.nih.gov/pubmed/?term=10853635 Longitudinal Data Analysis: Paired Eyes .   No .
Patient Care reading medical literature;guides;diagnostic test;patient care Journal Article Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8309035 Reading medical literature .   No .
Patient Care reading medical literature;therapy; prevention;patient care Journal Article Users' guides to the medical literature. II. How to use an article about therapy or prevention. B. What were the results and will they help me in caring for my patients? Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8258890 Reading Medical Literature .   No .
Permutation Tests permutation tests Journal Article Pros and cons of permutation tests in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=10814980 Permutation tests .   No .
Pharmacokinetic Models pharmacokinetic models; PK; one compartment model; two compartment model; three compartment model; Website Fitting Compartment Models Using PROC NLMIXED https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1883-2018.pdf The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure). 4 SAS No 4
Phase Ii Trials phase II trials;data Journal Article Comparison of error rates in single-arm versus randomized phase II cancer clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=20212253 Phase II Trials .   No .
Phase Ii Trials phase II trials;data Journal Article Randomized phase II trials: a long-term investment with promising returns. https://www.ncbi.nlm.nih.gov/pubmed/?term=21709274 Phase II Trials .   No .
Phase Ii Trials phase II trials;randomization;data Journal Article Randomized phase II trials: what does randomization gain? https://www.ncbi.nlm.nih.gov/pubmed/15699476 Phase II Trials .   No .
Phase Ii Trials phase II trials;data Journal Article Planned versus attained design in phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1604065 Phase II Trials .   No .
Phase Ii Trials phase II trials;data;design Journal Article Planned versus attained design in phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1604065 Phase II Trials .   No .
Phase Ii Trials phase II trials;design;data Journal Article Optimal two-stage designs for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=2702835 Phase II Trials .   No .
Phd PhD; Public Health; Doctor degree Online Interactive Course Doctor of Public Health  https://www.jhsph.edu/academics/degree-programs/doctoral-programs/doctor-of-public-health/ Doctor of Public Health offered by JHU/ Bloomberg School of Public Health 4 Any, SAS Yes .
Pk pharmacokinetic models; PK; one compartment model; two compartment model; three compartment model; Website Fitting Compartment Models Using PROC NLMIXED https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1883-2018.pdf The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT® 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples. Related concepts are also discussed, such as the %PKCONVRT autocall macro (which converts PK data sets that are stored according to industry standard to data sets that can be directly used by PROC NLMIXED), extension to Emax models, prediction, visualization, and fitting Bayesian PK models (by using the MCMC procedure). 4 SAS No 4
Placebo Effects placebo effects Journal Article The Powerful Placebo Effect: Fact or Fiction? https://www.jclinepi.com/article/S0895-4356(97)00203-5/abstract Placebo Effects .   No .
Placebo Effects Placebo Effects Journal Article Is the placebo powerless? An analysis of clinical trials comparing placebo with no treatment. https://www.ncbi.nlm.nih.gov/pubmed/?term=11372012 Placebo Effects .   No .
Planning clinical trial;planning; Journal Article Planning the size and duration of a clinical trial studying the time to some critical event. https://www.ncbi.nlm.nih.gov/pubmed/?term=4592596 Planning the size and duration of a clinical trial studying the time to some critical event. .   No .
Poisson Negative Binomial Regression Poisson negative binomial regression Journal Article Analysis of exacerbation rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN study. https://www.ncbi.nlm.nih.gov/pubmed/?term=17230434 Poisson/Negative binomial regression .   No .
Poisson Negative Bionmial Regression poisson negative bionmial regression Journal Article Sample size calculation for comparing two negative binomial rates https://www.ncbi.nlm.nih.gov/pubmed/24038204 Poisson/Neg Binomial Regression .   No .
Polynomial Regression biology; textbook; goodness of fit; data transformations; multiple comparisons; meta analysis; logistic regression; linear regression; correlation; polynomial regression; multiple regression; analysis of covariance Book Handbook of Biological Statistics http://www.biostathandbook.com/ This is an online textbook by John H. McDonald. This online textbook evolved from a set of notes for his Biological Data Analysis class at the University of Delaware. His main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. In his class and in this textbook, he spends relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. 1 Any No 1
Population sample; population Journal Article Statistics review 2: Samples and populations https://www.ncbi.nlm.nih.gov/pubmed/11983040 The previous review in this series introduced the notion of data description and outlined some of the more common summary measures used to describe a dataset. However, a dataset is typically only of interest for the information it provides regarding the population from which it was drawn. The present review focuses on estimation of population values from a sample. 1   No .
Power power; sample size Journal Article Statistics review 4: Sample size calculations https://www.ncbi.nlm.nih.gov/pubmed/12225610 The present review introduces the notion of statistical power and the hazard of under-powered studies. The problem of how to calculate an ideal sample size is also discussed within the context of factors that affect power, and specific methods for the calculation of sample size are presented for two common scenarios, along with extensions to the simplest case. 2   No 2
Power power;sample size;software Software to Download G*Power http://www.gpower.hhu.de/ G*Power is a tool to compute statistical power analyses for many different t tests, F tests, chi-sq tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses. 4 Any, SAS, SPSS, R, No 2
Power Cochran-Mantel-Haenszel test; power Website Introduction to the Cochran-Mantel-Haenszel Test https://cran.r-project.org/web/packages/samplesizeCMH/vignettes/samplesizeCMH-introduction.html The Cochran-Mantel-Haenszel test (CMH) is an inferential test for the association between two binary variables, while controlling for a third confounding nominal variable. A good introduction to the test along with R code for various aspects of the test including sample size/power calculations. 3 R No 3
Power power; sample size; calculator; multivariate Interactive Program GLIMPPSE online Power and Sample Size Calculation http://glimmpse.samplesizeshop.org Well-documented online power calculator with guided steps. "GLIMMPSE can compute power or sample size for univariate and multivariate linear models with Gaussian errors." 59-page user manual is available at http://samplesizeshop.org/files/2012/08/GLIMMPSEUserManual_v2.0.0.pdf. 4   No 1
Power power;sample size;bioequivalence Journal Article Sample size determination in stratified trials to establish the equivalence of two treatments. https://www.ncbi.nlm.nih.gov/pubmed/?term=8677403 Power and Sample size Bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article Sample size requirements for evaluating a conservative therapy. https://www.ncbi.nlm.nih.gov/pubmed/?term=688245 Power and sample size: Bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article Comparison of tests and sample size formulae for proving therapeutic equivalence based on the difference of binomial probabilities. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481195 Power/sample size: bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article "Proving the null hypothesis" in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7160191 Power/sample size: bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article Sample size determination for proving equivalence based on the ratio of two means for normally distributed data. https://www.ncbi.nlm.nih.gov/pubmed/?term=9990695 Power and sample size: bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article Estimation and sample size considerations for clustered binary responses. https://www.ncbi.nlm.nih.gov/pubmed/?term=7973205 Power and sample size; bioequivalence .   No .
Power power;sample size;bioequivalence Journal Article Sample size calculations for clustered binary data. https://www.ncbi.nlm.nih.gov/pubmed/?term=11427953 Power and sample size;bioequivalence .   No .
Power power;sample size;compliance Journal Article Estimating the power of compliance-improving methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=11146148 Power and sample size: compliance .   No .
Power power;sample size;momma distributions Journal Article Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events. ACIP Investigators. Asymptomatic Cardiac Ischemia Pilot. https://www.ncbi.nlm.nih.gov/pubmed/?term=8047740 Power and sample size; Counts of events/Gomma Distributions .   No .
Power power;sample size;contingency tables Journal Article Power evaluation of small drug and vaccine experiments with binary outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9463854 Power/Sample size: Contingency Tables .   No .
Power power;sample size;contingency tables Journal Article Sample size determination based on Fisher's Exact Test for use in 2 x 2 comparative trials with low event rates. https://www.ncbi.nlm.nih.gov/pubmed/?term=1316828 Power/sample Size: contingency tables .   No .
Power power;sample size;contengency tables Journal Article Power of testing proportions in small two-sample studies when sample sizes are equal. https://www.ncbi.nlm.nih.gov/pubmed/?term=8516594 Power/Sample size: Contingency Tables .   No .
Power power;sample size;general Journal Article Introduction to sample size determination and power analysis for clinical trials https://www.ncbi.nlm.nih.gov/pubmed/?term=7273794 Power/Sample size: general .   No .
Power power;sample size Journal Article Application of GEE procedures for sample size calculations in repeated measures experiments https://www.ncbi.nlm.nih.gov/pubmed/?term=9699236 Power/Sample size: general .   No .
Power power;sample size Journal Article Approaches to sample size estimation in the design of clinical trials--a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8235182 Power/Sample size: general .   No .
Power power;sample size Journal Article Planning and revising the sample size for a trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=7569499 Power/Sample size:general .   No .
Power power;sample size Journal Article Sample-size calculation for a log-transformed outcome measure. https://www.ncbi.nlm.nih.gov/pubmed/?term=10588295 Power/Sample size:general .   No .
Power power;sample size Journal Article The quest for "power": contradictory hypotheses and inflated sample sizes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9674660 Power/sample size: general .   No .
Power power;sample size Journal Article Estimating sample size for epidemiologic studies: the impact of ignoring exposure measurement uncertainty. https://www.ncbi.nlm.nih.gov/pubmed/?term=9682326 Power/Sample size: general .   No .
Power power;sample size Journal Article Design for sample size re-estimation with interim data for double-blind clinical trials with binary outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9304763 Power/sample size: general .   No .
Power power;sample size Journal Article Additivity test for composition of binomial effects in chromosome aberrations after radiation injury. https://www.ncbi.nlm.nih.gov/pubmed/?term=8532987 Power/Sample size: general .   No .
Power power;sample size Journal Article Sample size and power for prospective analysis of relative risk. https://www.ncbi.nlm.nih.gov/pubmed/?term=8511445 Power and sample size: general .   No .
Power power;sample size Journal Article Internal pilot studies for estimating sample size. https://www.ncbi.nlm.nih.gov/pubmed/7701146 Power and sample size: general .   No .
Power power;sample size Journal Article Power and sample size calculations for exact conditional tests with ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/?term=8369392 Power and sample size: general .   No .
Power power;sample size Journal Article Sample size determination for group sequential clinical trials with immediate response. https://www.ncbi.nlm.nih.gov/pubmed/?term=1518999 Power and sample size: general .   No .
Power power;sample size Journal Article A criterion for the adequacy of a simple design when a complex model will be used for analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=1664791 Power and sample size: general .   No .
Power power;sample size Journal Article Sample size: clues, hints or suggestions. https://www.ncbi.nlm.nih.gov/pubmed/?term=4019709 Power and sample size; general .   No .
Power power;sample size Journal Article How many patients are necessary to assess test performance? https://www.ncbi.nlm.nih.gov/pubmed/?term=2403604 Power and sample size: general .   No .
Power power;sample size Journal Article Adjusting sample size for anticipated dropouts in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9564195 Power and sample size: general .   No .
Power power;sample size Other Getting Ready to Estimate Sample Size: Hypotheses and Underlying Principles https://beckercat.wustl.edu/cgi-bin/koha/opac-detail.pl?biblionumber=29572 Power and sample size: general .   No .
Power power;sample size Journal Article Sample izes for medical trials with special reference to long-term therapy. https://www.ncbi.nlm.nih.gov/pubmed/?term=5646357 Power and sample size: general .   No .
Power power;sample size;ordered;categorical;data Journal Article Sample size calculations for ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/8134732 Power/sample size: ordered categorical data .   No .
Power Analysis clustered trials; hierarchical models; power analysis Interactive Program Power analysis for group randomized trials https://ssc.researchmethodsresources.nih.gov/ssc/ NIH website about clustered randomized trials 2 Any No 1
Power Analysis power analysis; bias (epidemiology); model adequacy; type I error; cox proportional hazards models; logistic regression. Journal Article Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression https://academic.oup.com/aje/article/165/6/710/63906 Commentary and simulation about back-of-the-envelope power analyses for logistic and Cox regression. 2 Any No 1
Power Analysis effect size; power analysis; sample size Journal Article Measures of Clinical Significance https://www.ncbi.nlm.nih.gov/pubmed/14627890 This article outlines effect sizes for common situations in clinical research 2   No 1
Power Analysis design;sample size;power analysis; Journal Article Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification. https://www.ncbi.nlm.nih.gov/pubmed/?term=3567285%5Buid%5D When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates. .   No .
Power Analysis regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Power Calculations confidence limits;power calculations Journal Article Confidence limits vs power calculations. https://www.ncbi.nlm.nih.gov/pubmed/?term=8173005 Confidence limits vs power calculations. .   No .
Power Calculations power calculations;sample size;linear multivariate model;repeated measures Journal Article Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. https://www.ncbi.nlm.nih.gov/pubmed/?term=24790282 Power and sample size .   No .
Power Sample Size power sample size;regression;anova Journal Article Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system. https://www.ncbi.nlm.nih.gov/pubmed/?term=2507829 Power/sample size .   No .
Power Sample Size power sample size;analysis;variance design; Journal Article Overcoming feelings of powerlessness in "aging" researchers: a primer on statistical power in analysis of variance designs. https://www.ncbi.nlm.nih.gov/pubmed/?term=9100270 Power and Sample size .   No .
Power Sample Size power sample size;reliability Journal Article Sample size and optimal designs for reliability studies. https://www.ncbi.nlm.nih.gov/pubmed/9463853 Power Sample Size: Reliability Study .   No .
Power Sample Size power sample size;reliability Journal Article Sample size requirements for reliability studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=3629046 Power Sample Size: Reliability Studies .   No .
Power Sample Size power sample size;repeated meaures Journal Article Planning group sizes in clinical trials with a continuous outcome and repeated measures. https://www.ncbi.nlm.nih.gov/pubmed/?term=10070672 Power Sample Size: Repeated Measures .   No .
Power Sample Size power sample size;repeated measures Journal Article Estimating sample sizes for repeated measurement designs. https://www.ncbi.nlm.nih.gov/pubmed/8205802 Power sample size: repeated measures .   No .
Power Sample Size power sample size;regression Journal Article Robustness and power of analysis of covariance applied to data distorted from normality by floor effects: homogeneous regression slopes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8668873 Power Sample size: regression .   No .
Power Sample Size power sample size;regression Journal Article Computing Confidence Bounds for Power and Sample Size of the General Linear Univariate Model. https://www.ncbi.nlm.nih.gov/pubmed/24039272 Power sample size: Regression .   No .
Power Sample Size power sample size;regression Journal Article A note on sample size computation for testing interactions. https://www.ncbi.nlm.nih.gov/pubmed/?term=3368673 Power sample size: Regression .   No .
Power Sample Size power sample size;regression Journal Article Power and sample size calculations for studies involving linear regression. https://www.ncbi.nlm.nih.gov/pubmed/9875838 Power Sample Size: Regression .   No .
Power Sample Size power sample size;ordered;categorical data Journal Article Sample size calculations for paired or matched ordinal data. https://www.ncbi.nlm.nih.gov/pubmed/9699235 Power Sample Size: Ordered Categorical Data .   No .
Preplanned MetaAnalysis;Preplanned Journal Article Hospital Outcomes Project for the Elderly (HOPE): Rationale and Design for a Prospective Poooled Analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=8440849 MetaAnalysis Pre-planned .   No .
Preplanned MetaAnalysis;Preplanned Journal Article Lessons learned from a prospective meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=7706636 MetaAnalysis Pre-planned .   No .
Presentation Of Data distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Prevention reading medical literature;therapy; prevention;patient care Journal Article Users' guides to the medical literature. II. How to use an article about therapy or prevention. B. What were the results and will they help me in caring for my patients? Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8258890 Reading Medical Literature .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Reporting of noninferiority and equivalence randomized trials: an extension of the CONSORT statement. https://www.ncbi.nlm.nih.gov/pubmed/16522836 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Lessons from and cautions about noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16522840 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority / Equivalence Trials - Bioequivalence .   No .
Priority priority;equivalence;trials;bioequivalence Journal Article An approximate unconditional test of non-inferiority between two proportions. https://www.ncbi.nlm.nih.gov/pubmed/?term=10931513 Nonin Priority/ Equivalence Trials - Bioequivalence .   No .
Priority priority;equivalence;trials;bioequivalence Journal Article Equivalence Trials https://www.ncbi.nlm.nih.gov/pubmed/?term=9329939 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Significance testing to establish equivalence between treatments, with special reference to data in the form of 2X2 tables. https://www.ncbi.nlm.nih.gov/pubmed/?term=588654 Nonin Priority/Equivalence Trials - Bioequivalence .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Bioequivalence revisited. https://www.ncbi.nlm.nih.gov/pubmed/?term=1485060 Nonin priority/Equivalence trials - Bioequivalence. .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article Conventional null hypothesis testing in active control equivalence studies. https://www.ncbi.nlm.nih.gov/pubmed/8582153 Nonin priority/Equivalence Trials - Bioequivalence .   No .
Priority nonin;priority;equivalence;trials;bioequivalence Journal Article A comparison of continuous infusion of alteplase with double-bolus administration for acute myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/9340504 Nonin prioritiy/Equivalence Trials - bioequivalence .   No .
Priority nonin;priority;equivalence;trials Journal Article Good enough: a primer on the analysis and interpretation of noninferiority trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16818930 Nonin Priority/Equivalence Trials .   No .
Priority nonin;priority;equivalence;trials Journal Article Alternatives to Hazard Ratios for Comparing the Efficacy or Safety of Therapies in Noninferiority Studies. https://www.ncbi.nlm.nih.gov/pubmed/26054047 Nonin Priority/Equivalence Trials .   No .
Priority nonin;priority;equivalence;trials Journal Article Non-inferiority trials: design concepts and issues - the encounters of academic consultants in statistics. https://www.ncbi.nlm.nih.gov/pubmed/?term=12520555 Nonin Priority/Equivalence Trials .   No .
Priority nonin;priority;equivalence;trials Journal Article Implementation of an adaptive group sequential design in a bioequivalence study. https://www.ncbi.nlm.nih.gov/pubmed/?term=17436336 Nonin Priority/Equivalence Trials .   No .
Priority nonin;priority;equivalence;trials Journal Article Quality of reporting of noninferiority and equivalence randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/16522835 Nonin Priority/Equivalence Trials .   No .
Probabilistic Graphical Models Probabilistic Graphical Models; Graphics; probability Online Interactive Course Probabilistic Graphical Models Specialization https://www.coursera.org/specializations/probabilistic-graphical-models Per the course website, this is a 3-course specialiation prepared by Stanford University to provide an understaing about probabilistic graphical models (PGMs) as a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. . Any Yes .
Probability Probability; Statistics Online Interactive Course Introduction to Probability and Data https://www.coursera.org/learn/probability-intro Per the course website, this non degree course offered by Duke University, introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization. . Any Yes .
Probability Probabilistic Graphical Models; Graphics; probability Online Interactive Course Probabilistic Graphical Models Specialization https://www.coursera.org/specializations/probabilistic-graphical-models Per the course website, this is a 3-course specialiation prepared by Stanford University to provide an understaing about probabilistic graphical models (PGMs) as a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. . Any Yes .
Probability probability;competing risks;Kaplan-Meier; Journal Article Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. https://www.ncbi.nlm.nih.gov/pubmed/?term=10204198 A topic that has received attention in both the statistical and medical literature is the estimation of the probability of failure for endpoints that are subject to competing risks. Despite this, it is not uncommon to see the complement of the Kaplan-Meier estimate used in this setting and interpreted as the probability of failure. If one desires an estimate that can be interpreted in this way, however, the cumulative incidence estimate is the appropriate tool to use in such situations. We believe the more commonly seen representations of the Kaplan-Meier estimate and the cumulative incidence estimate do not lend themselves to easy explanation and understanding of this interpretation. We present, therefore, a representation of each estimate in a manner not ordinarily seen, each representation utilizing the concept of censored observations being 'redistributed to the right.' We feel these allow a more intuitive understanding of each estimate and therefore an appreciation of why the Kaplan-Meier method is inappropriate for estimation purposes in the presence of competing risks, while the cumulative incidence estimate is appropriate. .   No .
Probability Kaplan-Meier; probability;competing risks Journal Article Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? https://www.ncbi.nlm.nih.gov/pubmed/?term=8516591 In the context of competing risks the Kaplan-Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two-sample test statistics are also presented. .   No .
Probability probability Journal Article The relative risk of incident coronary heart disease associated with recently stopping the use of beta-blockers. https://www.ncbi.nlm.nih.gov/pubmed/?term=1968518 We conducted a population-based, case-control study of risk factors for first events of coronary heart disease in patients with high blood pressure. All subjects had hypertension treated with medication. The 248 cases presented with new coronary heart disease from 1982 through 1984, and the 737 controls were a probability sample of health maintenance organization patients free of coronary heart disease. The health maintenance organization's computerized pharmacy database identified recent stoppers--patients who did not fill their prescriptions regularly enough to be at least 80% compliant. After adjustment for potential confounding factors, subjects who had recently stopped using beta-blockers had a transient fourfold increase in the relative risk of coronary heart disease (relative risk, 4.5; 95% confidence interval, 1.1 to 18.5). The association was specific to beta-blockers but not diuretics. A withdrawal syndrome immediately following the cessation of beta-blocker use may be an acute precipitant of angina and myocardial infarction in hypertensive patients who have no prior history of coronary heart disease. .   No .
Problem MetaAnalysis;Problem Journal Article Expressing the magnitude of adverse effects in case-control studies: "the number of patients needed to be treated for one additional patient to be harmed". https://www.ncbi.nlm.nih.gov/pubmed/?term=10678870 MetaAnalysis: Problem .   No .
Problem MetaAnalysis;Problem Journal Article Has the use of meta-analysis enhanced our understanding of terapies for postoperative nausea and vomiting? https://www.ncbi.nlm.nih.gov/pubmed/?term=10357318 MetaAnalysis: Problem .   No .
Problem MetaAnalysis;Problem Journal Article Measuring inconsistency in meta-analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=12958120 MetaAnalysis: Problem .   No .
Problem MetaAnalysis;Problem Journal Article Spurious precision? Meta-analysis of observational studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=9462324 MetaAnalysis: Problem .   No .
Proc Sql SAS; SAS macro; PROC SQL; SAS statistics Interactive Program Data Science with SAS Certification Training https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-excel-training Gain an understanding of SAS, statistics, PROC SQL, SAS macros 4 SAS Yes .
Programming SPSS; programming Online Interactive Course Getting started with SPSS http://www.open.edu/openlearn/people-politics-law/politics-policy-people/sociology/getting-started-spss/content-section-0?active-tab=description-tab Step-by-step approach to statistics software through seven interactive activities . SPSS No .
Programming SPSS; programming Online Interactive Course SPSS https://stats.idre.ucla.edu/spss/ Tips to learn SPSS . SPSS No .
Programming SPSS; programming Website SPSS for windows http://www.psych.utoronto.ca/courses/c1/spss/page1.htm SPSS tutorial for windows, learning SPSS basics . SPSS No .
Propensity Score propensity score; observational study Journal Article Propensity score matching: a conceptual review for radiology researchers https://www.ncbi.nlm.nih.gov/pubmed/25741190 The purpose of this article is to provide a step-by-step nonmathematical conceptual guide to propensity score analysis with particular emphasis on propensity score matching. 2   No 2
Propensity Score Methods observational data; propensity score methods; cost-effectiveness analysis Book Analysis of Observational Health Care Data Using SAS https://www.amazon.com/Analysis-Observational-Health-Care-Using/dp/1607642271/ref=sr_1_1?s=books&ie=UTF8&qid=1515099599&sr=1-1&keywords=analysis+of+observational+health+care+data+using+sas analysis of Observational Health Care Data Using SAS with many SAS code examples, includes examples of propensity score methods and cost-effectiveness analysis, etc. very practical and useful. 4 SAS Yes 4
Propensity Scores Propensity scores; confounding by indication Journal Article J Haukoos and R Lewis. The Propensity Score JAMA 2015 https://jamanetwork.com/journals/jama/fullarticle/2463242 A non-mathematical introduction to propensity scores 1 Any No 1
Propensity Scores Propensity scores;methods Journal Article Propensity score methods. https://www.ncbi.nlm.nih.gov/pubmed/20103037 Propensity scores .   No .
Propensity Scores propensity scores;surgical methods Other using Propensity Scores to Adjust for Group Differences: Examples comparing Alternative Surgical Methods http://www2.sas.com/proceedings/sugi25/25/st/25p261.pdf Propensity Scores .   No .
Propensity Scores propensity scores Journal Article Propensity Scores https://www.ncbi.nlm.nih.gov/pubmed/?term=18678805 Propensity Scores .   No .
Propensity Scores Propensity scores Other Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques http://www2.sas.com/proceedings/sugi26/p214-26.pdf Propensity Scores .   No .
Propensity Scores Propensity scores Journal Article The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. https://www.ncbi.nlm.nih.gov/pubmed/24122911 propensity scores .   No .
Proportional Hazard Model survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Proportional Hazards Models Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Proportions contingency table; measure of association; chi square; proportions Journal Article Statistics review 8: Qualitative data - tests of association https://www.ncbi.nlm.nih.gov/pubmed/14975045 This review introduces methods for investigating relationships between two qualitative (categorical) variables. The chi square test of association is described, together with the modifications needed for small samples. The test for trend, in which at least one of the variables is ordinal, is also outlined. Risk measurement is discussed. The calculation of confidence intervals for proportions and differences between proportions are described. Situations in which samples are matched are considered. 2   No .
Psychometrics network analysis; psychopathology; latent variable models; psychometrics Journal Article Network analysis: an integrative approach to the structure of psychopathology. https://www.ncbi.nlm.nih.gov/pubmed/23537483 In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry -> insomnia -> fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions. 3   No 1
Psychopathology network analysis; psychopathology; latent variable models; psychometrics Journal Article Network analysis: an integrative approach to the structure of psychopathology. https://www.ncbi.nlm.nih.gov/pubmed/23537483 In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry -> insomnia -> fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions. 3   No 1
Public Health PhD; Public Health; Doctor degree Online Interactive Course Doctor of Public Health  https://www.jhsph.edu/academics/degree-programs/doctoral-programs/doctor-of-public-health/ Doctor of Public Health offered by JHU/ Bloomberg School of Public Health 4 Any, SAS Yes .
Publication Bias publication bias Journal Article Publication bias in clinical research. https://www.ncbi.nlm.nih.gov/pubmed/1672966 Publication bias .   No .
Publication Bias Publication bias Journal Article Publication bias in editorial decision making. https://www.ncbi.nlm.nih.gov/pubmed/?term=12038924 Publication bias .   No .
Publication Bias Publication bias Journal Article Almost all articles on cancer prognostic markers report statistically significant results. https://www.ncbi.nlm.nih.gov/pubmed/?term=17981458 Publication bias .   No .
Publication Bias Publication bias Journal Article Selective publication of antidepressant trials and its influence on apparent efficacy. https://www.ncbi.nlm.nih.gov/pubmed/?term=18199864 Publication bias .   No .
Publication Bias publication bias Journal Article Assessment of publication bias in meta-analyses of cardiovascular diseases. https://www.ncbi.nlm.nih.gov/pubmed/?term=16166360 Publication bias .   No .
Publication Bias Publication bias Journal Article Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. https://www.ncbi.nlm.nih.gov/pubmed/17517853 Publication bias .   No .
Publication Bias publication bias; Journal Article Publication bias in clinical research https://www.ncbi.nlm.nih.gov/pubmed/?term=1672966 Publication bias .   No .
Publication Bias publication bias;editorial decision Journal Article Publication bias in editorial decision making. https://www.ncbi.nlm.nih.gov/pubmed/?term=12038924 Publication Bias .   No .
Publication Bias publication bias Journal Article Almost all articles on cancer prognostic markers report statistically significant results. https://www.ncbi.nlm.nih.gov/pubmed/?term=17981458 Publication Bias .   No .
Publication Bias publication bias;trials;efficacy Journal Article Selective publication of antidepressant trials and its influence on apparent efficacy. https://www.ncbi.nlm.nih.gov/pubmed/?term=18199864 Publication bias .   No .
Publication Bias publication bias Journal Article Assessment of publication bias in meta-analyses of cardiovascular diseases. https://www.ncbi.nlm.nih.gov/pubmed/?term=16166360 Publication Bias .   No .
Publication Bias publication bias Journal Article Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. https://www.ncbi.nlm.nih.gov/pubmed/?term=17517853 Publication bias .   No .
Publication Bias publication bias Journal Article A comparison of methods to detect publication bias in meta-analysis https://www.ncbi.nlm.nih.gov/pubmed/?term=12111913 Publication Bias .   No .
Publication Bias publication bias;clinical trials Journal Article Publication bias and clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/3442991 Publication bias .   No .
Publication Bias publication bias;meta-analysis;data Journal Article Should unpublished data be included in meta-analyses? Current convictions and controversies https://www.ncbi.nlm.nih.gov/pubmed/8492400 Publication Bias .   No .
Publishing Standards Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Python python Online Interactive Course Applied Data Science with Python Specialization https://www.coursera.org/specializations/data-science-python Per the course website, this 5-course specialiationThe 5 courses prepared by the University of Michigan introduces learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. . Any Yes .
Python python Online Interactive Course Introduction to Data Science in Python https://www.coursera.org/learn/python-data-analysis Per the course website, this is course 1 of 5 in Applied Data Science with Python Specialization prepared by the University of Michigan. It will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. . Any Yes .
Python Data Interpretation; Data analysis; python Online Interactive Course Data Analysis and Interpretation Specialization https://www.coursera.org/specializations/data-analysis Per the course website, this 5-course specialiation is prepared by the Wesleyan University. It helps you learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. . SPSS No .
Qc QC;clinical trials Journal Article An examination of the efficiency of some quality assurance methods commonly employed in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=pmid%3A+2345830 QC in clinical trials .   No .
Qc qc;clinical trials Journal Article Guidelines for quality assurance in multicenter trials: a position paper. https://www.ncbi.nlm.nih.gov/pubmed/9741868 QC in clinical trials .   No .
Qc qc;clinical trials Journal Article The other side of clinical trial monitoring; assuring data quality and procedural adherence. https://www.ncbi.nlm.nih.gov/pubmed/?term=17170037 QC in clinical trials .   No .
Qc qc;clinical trials Journal Article Double data entry: what value, what price? https://www.ncbi.nlm.nih.gov/pubmed/9492966 QC in clinical trials .   No .
Qc qc;clinical trials Journal Article Short report: Piloting paperless data entry for clinical research in Africa. https://www.ncbi.nlm.nih.gov/pubmed/?term=15772326 QC in clinical trials .   No .
Quality Control Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Quantitative Method methodology;randomized control trials;quantitative method; Journal Article A quality assessment of randomized control trials of primary treatment of breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=3711962 The methodology of randomized control trials (RCTs) of the primary treatment of early breast cancer has been reviewed using a quantitative method. Sixty-three RCTs comparing various treatment modalities tested on over 34,000 patients and reported in 119 papers were evaluated according to a standardized scoring system. A percentage score was developed to assess the internal validity of a study (referring to the quality of its design and execution) and its external validity (referring to presentation of information required to determine its generalizability). An overall score was also calculated as the combination of the two. The mean overall score for the 63 RCTs was 50% (95% confidence interval [CI] = 46% to 54%) with small and nonstatistically significant differences between types of trial. The most common methodologic deficiencies encountered in these studies were related to the randomization process (only 27 of the 63 RCTs adopted a truly blinded procedure), the handling of withdrawals (only 26 RCTs included all patients in the analyses), the description of the follow-up schedule (only 12 RCTs reported adequately), the report of side effects (adequate information given in 33 RCTs), and the description of the patient population (satisfactory in 29 RCTs). Telephone calls to the principal investigators improved the quality scores by seven points on a scale of 100, indicating that some of the deficiencies lay in reporting rather than performance. There was evidence that quality has improved over time and that the increasing tendency of involving a biostatistician in the research team was positively associated with the improvement of the internal validity but not with the external. .   No .
R R; R programming Online Interactive Course Introduction to R https://www.datacamp.com/courses/free-introduction-to-r Learning the basics in R programming . R No .
R R; R programming Online Interactive Course R studio https://www.rstudio.com/online-learning/ R studio online learning . R No .
R R; R programming Online Interactive Course Introduction to R for Data Science https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-7 Master the basics in R . R No .
R R; R programming Online Interactive Course R Basics - R Programming Language Introduction https://www.udemy.com/r-basics/ learn about the basic structure of R including packages . R No .
R R; R programming Online Interactive Course Try R https://www.codeschool.com/courses/try-r Learn the R programming language for data analysis and visualization. This software programming language is great for statistical computing and graphics. . R No .
R R; R programming Online Interactive Course R Training and Tutorials https://www.lynda.com/R-training-tutorials/1570-0.html Learn how to use R, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. .   Yes .
R R; R programming Website Introduction to R Seminar https://stats.idre.ucla.edu/r/seminars/intro/ Tips about R programming. . R No .
R SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
R Statistics; R Online Interactive Course Statistics with R Specialization https://www.coursera.org/specializations/statistics Per the course website, this non degree course offered by Duke University helps to learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. . R Yes .
R R Online Interactive Course R Programming https://www.coursera.org/learn/r-programming Per the course website, this course prepared by JHU will help learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. . R Yes .
R R; software development Online Interactive Course Mastering Software Development in R Specialization https://www.coursera.org/specializations/r Per the course website, this is a 5-course specialiation prepared by JHU. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. . R Yes .
R Capstone R Capstone; Statistics Online Interactive Course Statistics with R Capstone https://www.coursera.org/learn/statistics-project Per the course website, the capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. . Any Yes .
R Markdown data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
R Programming R; R programming Online Interactive Course Introduction to R https://www.datacamp.com/courses/free-introduction-to-r Learning the basics in R programming . R No .
R Programming R; R programming Online Interactive Course R studio https://www.rstudio.com/online-learning/ R studio online learning . R No .
R Programming R; R programming Online Interactive Course Introduction to R for Data Science https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-7 Master the basics in R . R No .
R Programming R; R programming Online Interactive Course R Basics - R Programming Language Introduction https://www.udemy.com/r-basics/ learn about the basic structure of R including packages . R No .
R Programming R; R programming Online Interactive Course Try R https://www.codeschool.com/courses/try-r Learn the R programming language for data analysis and visualization. This software programming language is great for statistical computing and graphics. . R No .
R Programming R; R programming Online Interactive Course R Training and Tutorials https://www.lynda.com/R-training-tutorials/1570-0.html Learn how to use R, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. .   Yes .
R Programming R; R programming Website Introduction to R Seminar https://stats.idre.ucla.edu/r/seminars/intro/ Tips about R programming. . R No .
R Programming data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
R Workflow data science; R programming; data visualization; R workflow; data transformations; R markdown; Book R for Data Science http://r4ds.had.co.nz/ This is the website has all of the content of the book "R for Data Science". It will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, vizualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, vizualising, and exploring data. 3 Any, R No 3
Random Coefficients Models multilevel analysis; hierarchical linear model; random coefficients models; intra-class correlation coefficient; Book Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling https://www.amazon.com/Multilevel-Analysis-Introduction-Advanced-Modeling/dp/184920201X/ref=mt_paperback?_encoding=UTF8&me= Introduction to multilevel analysis, topics include hierarchical linear model, random coefficients models, how to calculate ICC, how much does a model explain, etc. 5 SAS, SPSS, R, M+, Yes 4
Randomization randomization;design; Journal Article A method for assessing the quality of a randomized control trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=7261638 A system has been constructed to evaluate the design, implementation, and analysis of randomized control trials (RCT). The degree of quadruple blinding (the randomization process, the physicians and patients as to therapy, and the physicians as to ongoing results) is considered to be the most important aspect of any trial. The analytic techniques are scored with the same emphasis as is placed on the control of bias in the planning and implementation of the studies. Description of the patient and treatment materials and the measurement of various controls of quality have less weight. An index of quality of a RCT is proposed with its pros and cons. If published papers were to approximate these principles, there would be a marked improvement in the quality of randomized control trials. Finally, a reasonable standard design and conduct of trials will facilitate the interpretation of those with conflicting results and help in making valid combinations of undersized trials. .   No .
Randomization design; randomization;clinical trials Journal Article A new design for randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=431682 This paper proposes a new method for planning randomized clinical trials. This method is especially suited to comparison of a best standard or control treatment with an experimental treatment. Patients are allocated into two groups by a random or chance mechanism. Patients in the first group receive standard treatment; those in the second group are asked if they will accept the experimental therapy; if they decline, they receive the best standard treatment. In the analyses of results, all those in the second group, regardless of treatment, are compared with those in the first group. Any loss of statistical efficiency can be overcome by increased numbers. This experimental plan is indeed a randomized clinical trial and has the advantage that, before providing consent, a patient will know whether an experimental treatment is to be used. .   No .
Randomization randomization;trial Journal Article A randomized, controlled trial of aspirin in persons recovered from myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=6985998 The Aspirin Myocardial Infarction Study (AMIS) was a National Heart, Lung and Blood Institute-sponsored, multicenter, randomized, double-blind, and placebo-controlled trial designed to test whether the regular administration of aspirin to men and women who had experienced at least one documented myocardial infarction (MI) would result in a significant reduction in total mortality over a three-year period. Cause-specific mortality, nonfatal events, and side effects were also evaluated. Over a 13-month period, 4,524 persons between the ages of 30 and 69 years were randomized to either 1 g of aspirin per day (2,267 persons) or placebo (2,257 persons). High levels of patient compliance to study protocol were indicated by various measures. Total mortality during the entire follow-up period was 10.8% in the aspirin group and 9.7% in the placebo group. Three-year total mortality was 9.6% in the aspirin group and 8.8% in the placebo group. The percentage of definite nonfatal MI was 8.1% in the placebo group and 6.3% in the aspirin group. Coronary incidence (coronary heart disease mortality or definite nonfatal MI) was 14.1% in the aspirin group and 14.8% in the placebo group. Symptoms suggestive of peptic ulcer, gastritis, or erosion of gastric mucosa occurred in 23.7% of the aspirin group and 14.9% in the placebo group. Based on AMIS results, aspirin is not recommended for routine use in patients who have survived an MI. .   No .
Randomization methods;analysis;clinical trial;randomization; Journal Article Adjusting for non-compliance and contamination in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9160496 A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive. .   No .
Randomization randomization; Journal Article Adherence in the training levels comparison trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8775354 In the Training Levels Comparison Trial, 197 male coronary heart disease patients were randomized to low or high intensity training with target heart rates, which corresponded to 50% and 85% of the VO2max achieved on the previous exercise test, respectively. Patients were to exercise at their assigned intensity level at three 1-h long supervised sessions per week for 2 yr. This paper reports on two components of adherence: attendance at exercise sessions and achievement of heart rates in the target range. During the first year of training, the average percent of exercise sessions attended (mean +/- SE) for the low intensity group (64.0 +/- 2.5%) was significantly higher than for the high intensity group (55.5% +/- 2.7%). At the end of 1 yr of training, 54% and 37% of the low and high intensity patients, respectively, achieved heart rates within 5 beats.min-1 of their target heart rates. Although the low intensity program was preferable to achieve maximum attendance, attenders on the high intensity program achieved higher heart rates. These results suggest that to maximize the achieved heart rate, it would be optimal to motivate a cardiac rehabilitation patient to train at the high intensity level for a prolonged period of time. .   No .
Randomization controlled clinical trials;bias;randomization;bias Journal Article Bias in treatment assignment in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=6633598 Controlled clinical trials of the treatment of acute myocardial infarction offer a unique opportunity for the study of the potential influence on outcome of bias in treatment assignment. A group of 145 papers was divided into those in which the randomization process was blinded (57 papers), those in which it may have been unblinded (45 papers), and those in which the controls were selected by a nonrandom process (43 papers). At least one prognostic variable was maldistributed (P less than 0.05) in 14.0 per cent of the blinded-randomization studies, in 26.7 per cent of the unblinded-randomization studies, and in 58.1 per cent of the nonrandomized studies. Differences in case-fatality rates between treatment and control groups (P less than 0.05) were found in 8.8 per cent of the blinded-randomization studies, 24.4 per cent of the unblinded-randomization studies, and 58.1 per cent of the nonrandomized studies. These data emphasize the importance of keeping those who recruit patients for clinical trials from suspecting which treatment will be assigned to the patient under consideration. .   No .
Randomization clinical trial;randomization;design;variation Journal Article Complexity and contradiction in clinical trial research. https://www.ncbi.nlm.nih.gov/pubmed/?term=3548349 Randomized clinical trials have become the accepted scientific standard for evaluating therapeutic efficacy. Contradictory results from multiple randomized clinical trials on the same topic have been attributed either to methodologic deficiencies in the design of one of the trials or to small sample sizes that did not provide assurance that a meaningful therapeutic difference would be detected. When 36 topics with conflicting results that included over 200 randomized clinical trials in cardiology and gastroenterology were reviewed, it was discovered that results of randomized clinical trials often disagree because the complexity of the randomized clinical trial design and the clinical setting creates inconsistencies and variation in the therapeutic evaluation. Nine methodologic sources of this variation were identified, including six items concerned with the design of the trials, and three items concerned with interpretation. The design issues include eligibility criteria and the selection of study groups, baseline differences in the available population, variability in indications for the principal and concomitant therapies, protocol requirements of the randomized clinical trial, and management of intermediate outcomes. The issues in interpreting the trials include the regulatory effects of treatments, the frailty of double-blinding, and the occurrence of unexpected trial outcomes. The results of this review suggest that pooled analyses of conflicting results of randomized clinical trials (meta-analyses) may be misleading by obscuring important distinctions among trials, and that enhanced flexibility in strategies for data analysis will be needed to ensure the clinical applicability of randomized clinical trial results. .   No .
Randomization compliance;randomization;design;interpretation;clinical trials Journal Article Compliance with an experimental drug regimen for treatment of asthma: its magnitude, importance, and correlates. https://www.ncbi.nlm.nih.gov/pubmed/?term=6389582 This paper reports on data from a double-blind, randomized controlled study of out-patient use of corticosteroids following an acute asthma attack. Issues related to compliance are examined, including: (1) the extent of non-compliance; (2) impact of non-compliance on interpreting the drug trial results; and (3) correlates of non-compliance. Of the 102 cases enrolled in the study, 25.5% were excluded from analysis because they were lost to follow-up (10.8%) or non-compliers (14.7%). Based on data for compliers, the drugs were found to reduce relapse rates and asthma symptomatology; when non-compliers were included in the analysis, the steroid drug appeared ineffective for reducing relapses and less effective for improving overall illness status. Examination of 24 potential correlates of compliance yielded a few significant associations, and only the "usual habit of compliance" correlation suggests an avenue for future action. The implications of the study findings for design and interpretation of clinical trials, as well as for improved management of chronic diseases, are discussed. .   No .
Randomization clinical trials;randomization; Journal Article Deficiencies of clinical trials of alcohol withdrawal. https://www.ncbi.nlm.nih.gov/pubmed/?term=6342448 Eighty-one therapeutic trials of alcohol withdrawal were found that have been published in English since 1954; controls were randomized in 29 (RCTs). Two thousand three hundred thirteen patients were randomized. Variable pretreatment description prevented estimates of delirium tremens and convulsion prevalence, but only four deaths were reported. Endpoints were thus entirely subjective in these moderately ill patients. Protocol quality of the RCTs was graded by a previously developed system for evaluating adequacy of descriptions, blinding, and essential measurements. Mean score obtained was .49 +/- .03 (1 SE). (A perfect paper would score 1.00.) Data presentations and statistical analyses scored .18 +/- .03. There was little evidence of improvement of scores over time. Papers lacked confidence intervals, proper handling of dropouts, and adequate details of side effects. In five RCTs, six comparisons showed that benzodiazepines are clearly superior to placebo (p less than .001), but conclusions about comparisons with other drugs were not possible. In none of eight "negative" comparisons was the probability of a type II error (beta) considered. Discovery of more effective symptomatic agents or methods of reducing the death rate will require more rigid protocols and analyses as well as larger studies to allow the use of more critical endpoints such as occurrence of delirium tremens, convulsions, or death. .   No .
Randomization clinical trials;randomization Journal Article How should clinicians interpret clinical trials? https://www.ncbi.nlm.nih.gov/pubmed/?term=7585780 Given the rapid evolution of cardiovascular medicine, clinicians must sift through an enormous array of information about new therapies in order to determine how best to treat patients with ischemic heart disease. They should first consider the evidence from randomized clinical trials, because these trials eliminate bias and permit broad statistical analyses. If randomized clinical trial data are not available, next in order of the strength of their evidence are observational studies, historically controlled studies, case series, and case reports. Clinicians must additionally ascertain that an investigation has the elements of good design, including a clear question, adequate sample size, appropriate inclusion and exclusion criteria, evidence that the right amount of data was collected carefully, and allowances in the analyses for patients taking multiple therapies and randomized into several clinical trials. .   No .
Randomization randomization;evaluating treatment efficacy; Journal Article Influence of adherence to treatment and response of cholesterol on mortality in the coronary drug project. https://www.ncbi.nlm.nih.gov/pubmed/?term=6999345 The Coronary Drug Project was carried out to evaluate the efficacy and safety of several lipid-influencing drugs in the long-term treatment of coronary heart disease. The five-year mortality in 1103 men treated with clofibrate was 20.0 per cent, as compared with 20.9 per cent in 2789 men given placebo (P = 0.55). Good adherers to clofibrate, i.e., patients who took 80 per cent of more of the protocol prescription during the five-year follow-up period, had a substantially lower five-year mortality than did poor adherers to clofibrate (15.0 vs. 24.6 per cent; P = 0.00011). However, similar findings were noted in the placebo group, i.e., 15.1 per cent mortality for good adherers and 28.3 per cent for poor adherers (P = 4.7x10-16). These findings and various other analyses of mortality in the clofibrate and placebo groups of the project show the serious difficulty, if not impossibility, of evaluating treatment efficacy in subgroups determined by patient responses (e.g., adherence or cholesterol change) to the treatment protocol after randomization. .   No .
Randomization analysis;clinical trials;randomization;intent-to-treat; Journal Article Intention-to-treat vs. on-treatment analyses of clinical trial data: experience from a study of pyrimethamine in the primary prophylaxis of toxoplasmosis in HIV-infected patients. ANRS 005/ACTG 154 Trial Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=9620807 Randomized clinical trials analyzed by the intent-to-treat approach provide unbiased comparisons among treatment groups. This paper presents several approaches used for analyzing data of a recent trial and the difficulties encountered in interpreting the results of each approach. Although exploratory analyses may yield clinically relevant information and useful clarifications in the evaluation of treatments, intention-to-treat remains the only interpretable analysis of clinical trials. .   No .
Randomization randomization Journal Article Making do without randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8300649 Making do without randomised trials. .   No .
Randomization randomization Journal Article Randomised trial of prophylactic daily aspirin in British male doctors. https://www.ncbi.nlm.nih.gov/pubmed/?term=3125882 A six year randomised trial was conducted among 5139 apparently healthy male doctors to see whether 500 mg aspirin daily would reduce the incidence of and mortality from stroke, myocardial infarction, or other vascular conditions. Though total mortality was 10% lower in the treated than control group, this difference was not statistically significant and chiefly involved diseases other than stroke or myocardial infarction. Likewise, there was no significant difference in the incidence of non-fatal myocardial infarction or stroke--indeed, disabling strokes were somewhat commoner among those allocated aspirin. The lower confidence limit for the effect of aspirin on non-fatal stroke or myocardial infarction, however, was a substantial 25% reduction. Migraine and certain types of musculoskeletal pain were reported significantly less often in the treated than control group, but as the control group was not given a placebo the relevance of these findings was difficult to assess. There was no apparent reduction in the incidence of cataract in the treated group. The lack of any apparent reduction in disabling stroke or vascular death contrasts with the established value of antiplatelet treatment after occlusive vascular disease. .   No .
Randomization sample size;randomization Journal Article Statistical power, sample size, and their reporting in randomized controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8015121 To describe the pattern over time in the level of statistical power and the reporting of sample size calculations in published randomized controlled trials (RCTs) with negative results.Most trials with negative results did not have large enough sample sizes to detect a 25% or a 50% relative difference. This result has not changed over time. Few trials discussed whether the observed differences were clinically important. There are important reasons to change this practice. The reporting of statistical power and sample size also needs to be improved. .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Dynamic balanced randomization for clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8134737 Clinical Trials: Randomization Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Empirical Evidence of Bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7823387 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Ensuring Balanced Distribution of Prognostic Factors in Treatment Outcome Research. https://www.ncbi.nlm.nih.gov/pubmed/?term=7723001 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Generation of allocation sequences in randomized trials: chance, not choice. https://www.ncbi.nlm.nih.gov/pubmed/?term=11853818 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article How study design affects outcomes in comparisons of therapy. II: Surgical. https://www.ncbi.nlm.nih.gov/pubmed/?term=2727469 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article How to study design affects outcomes in comparisons of therapy. I: Medical https://www.ncbi.nlm.nih.gov/pubmed/?term=2727468 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Randomized versus Historical Controls for Clnical Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7058834 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Reporting Randomized Controlled Trials. An Experiment and a call for responses From Readers. https://www.ncbi.nlm.nih.gov/pubmed/?term=7897791 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Subverting Randomization in Controlled Trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7474192 Clinical Trials: Randomization/Control Groups .   No .
Randomization Clinical Trials;Randomization;Control Groups ;Adaptive Sampling Journal Article The randomization and stratification of patients to clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=4612056 Clinical Trials: Randomization/Control Groups & Adaptive Sampling .   No .
Randomization Clinical Trials;Randomization;Control Groups Journal Article Treatment allocation Methods in clinical trials: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=3895341 Clinical Trials: Randomization/Control Groups .   No .
Randomization Randomization Journal Article Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/1100130 Randomization .   No .
Randomization phase II trials;randomization;data Journal Article Randomized phase II trials: what does randomization gain? https://www.ncbi.nlm.nih.gov/pubmed/15699476 Phase II Trials .   No .
Randomized randomized; nonrandomized; treatment effects Journal Article Comparison of evidence of treatment effects in randomized and nonrandomized studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=11497536 randomized and nonrandomized studies .   No .
Randomized Clinical Trial randomized clinical trial;study design Journal Article Rationale and design of a randomized clinical trial on prevention of stroke in isolated systolic hypertension. The Systolic Hypertension in the Elderly Program (SHEP) Cooperative Research Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=2905387 Isolated systolic hypertension (ISH)--i.e. high systolic pressure with nonhypertensive (less than 90 mmHg) diastolic pressure--is a recognized risk factor for cardiovascular disease among individuals in the age group 60 years and above. This observation suggests that antihypertensive treatment might be beneficial. Results of the Systolic Hypertension in the Elderly Program Pilot Study (SHEP-PS) indicated the feasibility of a full-scale clinical trial on the efficacy of drug treatment of ISH. The Systolic Hypertension in the Elderly Program (SHEP) is a randomized, double-blind, placebo-controlled clinical trial with the primary objective of assessing the effect of drug treatment of ISH--systolic pressure 160-219 mmHg and diastolic pressure less than 90--on occurrence of fatal and nonfatal stroke. This multicenter clinical trial has a sample size of 4736 participants, with high statistical power to detect a reduction of 32% or more in the study's primary end point during the 4-6 year period of treatment and follow-up. Low dosage chlorthalidone is the main study drug. Further features of the design of SHEP and the trial's organization are described. .   No .
Randomized Clinical Trials randomized clinical trials; sample size; algorithm;design; Journal Article A comprehensive algorithm for determining whether a run-in strategy will be a cost-effective design modification in a randomized clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8446807 In randomized clinical trials, poor compliance and treatment intolerance lead to reduced between-group differences, increased sample size requirements, and increased cost. A run-in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run-in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step-by-step algorithm through which one can estimate the cost-effectiveness of a potential run-in. Because the cost-effectiveness of a run-in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run-in on the required number of randomized, eligible, and screened subjects. Run-in strategies are most likely to be cost-effective when: (1) per patient costs during the post-randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run-in is inexpensive; (5) for most patients, the run-in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run-in are treatment intolerant or non-compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost-effectiveness of run-in strategies are stringent. In particular, if the only purpose of a run-in is to exclude ordinary partial compliers, the run-in will frequently add to the cost of the trial. Often, the cost-effectiveness of a run-in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects. .   No .
Randomized Clinical Trials design; analysis;randomized clinical trials Journal Article Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. analysis and examples. https://www.ncbi.nlm.nih.gov/pubmed/?term=831755 Part II of report which describes efficient methods of analysis of randomized clinical trials in which we wish to compare the duration of survival (or the time until some other untoward event first occurs) among different groups of patients. .   No .
Randomized Clinical Trials randomized clinical trials Journal Article Do the results of randomized clinical trials of cardiovascular drugs influence medical practice? The SAVE Investigators. https://www.ncbi.nlm.nih.gov/pubmed/?term=1535419 Medical practice patterns change in response to a variety of stimuli, one of which may be the publication of the results of randomized clinical trials. We assessed the temporal association between the publication of clinical trials on myocardial infarction and changes in treatment practices for this disorder. These observations suggest that randomized clinical trials have a measurable influence on medical practice patterns. .   No .
Randomized Clinical Trials randomized clinical trials;bias;methodology;analysis Journal Article Intention-to-treat analysis in randomized trials: who gets counted? https://www.ncbi.nlm.nih.gov/pubmed/?term=9378838 This article discusses the rationale and implications associated with the selection and use of analysis strategies for randomized clinical trials as they relate to protocol deviations. The topics addressed specifically are the conceptual and methodologic approaches and biases of clinical efficacy and effectiveness assessment. The authors suggest that different analytic strategies may be more or less appropriate depending on the intended audience. .   No .
Randomized Clinical Trials compliance;randomized clinical trials; Journal Article Measurement of patient compliance and the interpretation of randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=1838332 The aim of this review is to demonstrate why the management of compliance, although not an explicit feature of the rules of Good Clinical Practice, is essential to the successful conduct of clinical trials and in correct interpretation of the results. .   No .
Randomized Clinical Trials Reading medical literature;randomized clinical trials Journal Article Beyond the randomized clinical trial: the role of effectiveness studies in evaluating cardiovascular therapies. https://www.ncbi.nlm.nih.gov/pubmed/?term=18794402 Medical literature:Randomized clinical trials .   No .
Randomized Control Trials methodology;randomized control trials;quantitative method; Journal Article A quality assessment of randomized control trials of primary treatment of breast cancer. https://www.ncbi.nlm.nih.gov/pubmed/?term=3711962 The methodology of randomized control trials (RCTs) of the primary treatment of early breast cancer has been reviewed using a quantitative method. Sixty-three RCTs comparing various treatment modalities tested on over 34,000 patients and reported in 119 papers were evaluated according to a standardized scoring system. A percentage score was developed to assess the internal validity of a study (referring to the quality of its design and execution) and its external validity (referring to presentation of information required to determine its generalizability). An overall score was also calculated as the combination of the two. The mean overall score for the 63 RCTs was 50% (95% confidence interval [CI] = 46% to 54%) with small and nonstatistically significant differences between types of trial. The most common methodologic deficiencies encountered in these studies were related to the randomization process (only 27 of the 63 RCTs adopted a truly blinded procedure), the handling of withdrawals (only 26 RCTs included all patients in the analyses), the description of the follow-up schedule (only 12 RCTs reported adequately), the report of side effects (adequate information given in 33 RCTs), and the description of the patient population (satisfactory in 29 RCTs). Telephone calls to the principal investigators improved the quality scores by seven points on a scale of 100, indicating that some of the deficiencies lay in reporting rather than performance. There was evidence that quality has improved over time and that the increasing tendency of involving a biostatistician in the research team was positively associated with the improvement of the internal validity but not with the external. .   No .
Randomized Control Trials randomized control trials;sample size;clinical trials Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. Survey of 71 "negative" trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Seventy-one "negative" randomized control trials were re-examined to determine if the investigators had studied large enough samples to give a high probability (greater than 0.90) of detecting a 25 per cent and 50 per cent therapeutic improvement in the response. Sixty-seven of the trials had a greater than 10 per cent risk of missing a true 25 per cent therapeutic improvement, and with the same risk, 50 of the trials could have missed a 50 per cent improvement. Estimates of 90 per cent confidence intervals for the true improvement in each trial showed that in 57 of these "negative" trials, a potential 25 per cent improvement was possible, and 34 of the trials showed a potential 50 per cent improvement. Many of the therapies labeled as "no different from control" in trials using inadequate samples have not received a fair test. Concern for the probability of missing an important therapeutic improvement because of small sample sizes deserves more attention in the planning of clinical trials. .   No .
Randomized Controlled Trial randomized controlled trial; bias;meta-analysis Journal Article Systematic review of the empirical evidence of study publication bias and outcome reporting bias. https://www.ncbi.nlm.nih.gov/pubmed/?term=18769481 The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting bias has received less attention. Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials. .   No .
Randomized Controlled Trials Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Randomized Controlled Trials randomized controlled trials;methodology Journal Article Assessing the quality of randomized controlled trials: an annotated bibliography of scales and checklists. https://www.ncbi.nlm.nih.gov/pubmed/?term=7743790 Assessing the quality of randomized controlled trials (RCTs) is important and relatively new. Quality gives us an estimate of the likelihood that the results are a valid estimate of the truth. We present an annotated bibliography of scales and checklists developed to assess quality. Twenty-five scales and nine checklists have been developed to assess quality. The checklists are most useful in providing investigators with guidelines as to what information should be included in reporting RCTs. The scales give readers a quantitative index of the likelihood that the reported methodology and results are free of bias. There are several shortcomings with these scales. Future scale development is likely to be most beneficial if questions common to all trials are assessed, if the scale is easy to use, and if it is developed with sufficient rigor. .   No .
Randomized Controlled Trials randomized controlled trials Journal Article Evaluation of the effectiveness of lipid-lowering therapy (bile acid sequestrants, niacin, psyllium and lovastatin) for treating hypercholesterolemia in veterans. https://www.ncbi.nlm.nih.gov/pubmed/?term=8456750 Veterans are frequently older, have more chronic illnesses, and take more medications than subjects volunteering for clinical trials. Because these factors may impair the effectiveness of lipid-lowering drug therapy, the effectiveness of drug therapy in veterans may differ from that measured in randomized controlled trials. In 297 patients with type IIa hyperlipidemia attending a large Veterans Administration Medical Center lipid clinic, adverse effects, compliance, lipid and lipoprotein responses to drug therapy were prospectively monitored. Bile acid sequestrants (4 packets/day) were associated with a high rate of adverse effects, and had the highest drug discontinuance rate (37%) and poorest compliance (73 +/- 3% of the doses prescribed reported ingested) of all agents. Patients aged > 60 years tolerated therapy with bile acid sequestrants less well than did younger veterans (p < 0.01). Niacin (1.5 g/day) also had a high drug discontinuance rate (27%). Lovastatin (20 mg/day) had the lowest drug discontinuance rate (2%) and the highest compliance (90 +/- 2%). Lovastatin also reduced low-density lipoprotein (LDL) cholesterol the most (-21.6 +/- 2.0%), whereas niacin produced the largest increase in high-density lipoprotein (HDL) cholesterol (+/- 14.3 +/- 2.2%); both niacin and lovastatin produced similar reductions in the LDL/HDL ratio. However, psyllium (10.4 g/day) reduced LDL cholesterol by only 2%, and had no effect on the LDL/HDL ratio. Psyllium produced larger LDL cholesterol reductions in patients aged < 60 years than in older patients (p < 0.01). Niacin and lovastatin are effective drugs for hypercholesterolemia management in the Veterans Administration Medical Center setting.(ABSTRACT TRUNCATED AT 250 WORDS). .   No .
Randomized Controlled Trials reading medical literature;randomized controlled trials Journal Article Transition from meeting abstract to full-length journal article for randomized controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16537738 Reading Medical Literature .   No .
Randomized Controlled Trials As Topic/Methods research Design Randomized Controlled Trials as Topic/methods Research Design Journal Article Non-inferiority trials: design concepts and issues - the encounters of academic consultants in statistics https://www.ncbi.nlm.nih.gov/pubmed/12520555 Clear description of when and how to use a non-inferiority study design 2 Any No 2
Randomized Efficacy statistical significance; randomized efficacy; trials Journal Article Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. https://www.ncbi.nlm.nih.gov/pubmed/9450711 statistical significance, randomized efficacy .   No .
Randomized Trials randomized trials Journal Article A clinician's guide for conducting randomized trials in individual patients. https://www.ncbi.nlm.nih.gov/pubmed/?term=3409138 In determining optimal treatment for a patient conventional trials of therapy are susceptible to bias. Large-scale randomized trials can provide only a partial guide and have not been or cannot be carried out for most clinical disorders. However, randomized controlled trials (RCTs) in individual patients (N of 1 RCTs) may in some circumstances provide a solution to this dilemma. In an N of 1 RCT a patient undergoes pairs of treatment periods (one period of each pair with the active drug and one with matched placebo, assigned at random); both the patient and the clinician are blind to allocation, and treatment targets are monitored. N of 1 RCTs are useful for chronic, stable conditions for which the proposed treatment, which has a rapid onset of action and ceases to act soon after it is discontinued, has shown promise in an open trial of therapy. The monitoring of treatment targets usually includes quantitative measurement of the patient's symptoms with the use of simple patient diaries or questionnaires. Pairs of treatment periods are continued until effectiveness is proved or refuted. The cooperation of a pharmacy is required for the preparation of matching placebos and conduct of the trial. Formal statistical analysis may be helpful for interpreting the results. The practical approach presented in this paper allows clinicians to conduct their own N of 1 RCTs. .   No .
Randomized Trials meta analyses; randomized trials Journal Article A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction. https://www.ncbi.nlm.nih.gov/pubmed/?term=1535110 To examine the temporal relationship between accumulating data from randomized control trials of treatments for myocardial infarction and the recommendations of clinical experts writing review articles and textbook chapters. .   No .
Randomized Trials methods;randomized trials;analysis; Journal Article A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8500308 The standard approach to analyzing randomized trials ignores information on postrandomization compliance. Application of these methods results in estimates that may lack the desired causal interpretation. We employ a new method of estimation and analyze data from the Multiple Risk Factor Intervention Trial (MRFIT) to estimate the causal effect of quitting cigarette smoking. Our procedure utilizes a method proposed by Robins and Tsiatis and allows us to take advantage of postrandomization smoking history without requiring untenable assumptions about the comparability of compliers and noncompliers. We contrast the performance of our method and the standard intent-to-treat analysis in the MRFIT data and in simulated data in which compliance rates are varied. .   No .
Randomized Trials randomized trials Journal Article A proposal for structured reporting of randomized controlled trials. The Standards of Reporting Trials Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=7990245 A proposal for structured reporting of randomized controlled trials. The Standards of Reporting Trials Group. .   No .
Randomized Trials analysis;randomized trials; Journal Article Analysis of data from group-randomized trials with repeat observations on the same groups. https://www.ncbi.nlm.nih.gov/pubmed/?term=9699231 This study used Monte Carlo simulations to evaluate the performance of alternative models for the analysis of group-randomized trials having more than two time intervals for data collection. The major distinction among the models tested was the sampling variance of the intervention effect. In the mixed-model ANOVA, the sampling variance of the intervention effect is based on the variance among group x time-interval means. In the random coefficients model, the sampling variance of the intervention effect is based on the variance among the group-specific slopes. These models are equivalent when the design includes only two time intervals, but not when there are more than two time intervals. The results indicate that the mixed-model ANOVA yields unbiased estimates of sampling variation and nominal type I error rates when the group-specific time trends are homogenous. However, when the group-specific time trends are heterogeneous, the mixed-model ANOVA yields downwardly biased estimates of sampling variance and inflated type I error rates. In contrast, the random coefficients model yields unbiased estimates of sampling variance and the nominal type I error rate regardless of the pattern among the groups. We discuss implications for the analysis of group-randomized trials with more than two time intervals. .   No .
Randomized Trials methodology;bias;randomized trials;design Journal Article Methodological bias in cluster randomised trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=15743523 Cluster randomised trials can be susceptible to a range of methodological problems. These problems are not commonly recognised by many researchers. In this paper we discuss the issues that can lead to bias in cluster trials. Methodological biases in the design and execution of cluster randomised trials is frequent. Some of these biases associated with the use of cluster designs can be avoided through careful attention to the design of cluster trials. Firstly, if possible, individual allocation should be used. Secondly, if cluster allocation is required, then ideally participants should be identified before random allocation of the clusters. Third, if prior identification is not possible, then an independent recruiter should be used to recruit participants. .   No .
Randomized Trials randomized trials Journal Article Persistence of contradicted claims in the literature. https://www.ncbi.nlm.nih.gov/pubmed/18056905 randomized trials .   No .
Range, Interquartile Range distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Rcts recruitment;screening;RCTs Journal Article Recruitment of elderly volunteers for a multicenter clinical trial: the SHEP pilot study. https://www.ncbi.nlm.nih.gov/pubmed/?term=3743091 Recruitment/Screening in RCT's .   No .
Rcts recruitment;screening;RCTs Journal Article Screening the elderly in the community: controlled trial of dependency surveillance using a questionnaire administered by volunteers. https://www.ncbi.nlm.nih.gov/pubmed/?term=2354297 Recruitment/Screening in RCT's .   No .
Rcts recruitment;screening;RCTs Journal Article Systolic Hypertension in the Elderly Program (SHEP). Part 2: Screening and recruitment. https://www.ncbi.nlm.nih.gov/pubmed/?term=1999371 Recruitment/Screening in RCTs .   No .
Reading Medical Literature reading medical literature;guides;diagnostic test;patient care Journal Article Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8309035 Reading medical literature .   No .
Reading Medical Literature reading medical literature;diagnostic test;study results Journal Article Users' Guides to the Medical Literature III. How to use an article about a diagnostic test A. Are the Results of the study valid? https://www.ncbi.nlm.nih.gov/pubmed/?term=8283589 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature;therapy; prevention;patient care Journal Article Users' guides to the medical literature. II. How to use an article about therapy or prevention. B. What were the results and will they help me in caring for my patients? Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/8258890 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature;harm Journal Article Users' guides to the medical literature. IV. How to use an article about harm. Evidence-Based Medicine Working Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=8182815 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature;clinical research Journal Article Contradicted and initially stronger effects in highly cited clinical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=16014596 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature; transnational research Journal Article Medicine. Life cycle of translational research for medical interventions. https://www.ncbi.nlm.nih.gov/pubmed/?term=18772421 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature;randomized controlled trials Journal Article Transition from meeting abstract to full-length journal article for randomized controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=16537738 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature;contradictions Journal Article Contradictions in Highly cited Clinical Research https://www.ncbi.nlm.nih.gov/pubmed/16333000 Reading medical literature .   No .
Reading Medical Literature reading medical literature;survival analysis Journal Article How quickly do systematic reviews go out of date? A survival analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=17638714 Reading Medical Literature .   No .
Reading Medical Literature reading medical literature Journal Article Persistence of Contradicted Claims in the Literature https://www.ncbi.nlm.nih.gov/pubmed/18056905 Reading Medical Literature .   No .
Reading Medical Literature Reading medical literature;randomized clinical trials Journal Article Beyond the randomized clinical trial: the role of effectiveness studies in evaluating cardiovascular therapies. https://www.ncbi.nlm.nih.gov/pubmed/?term=18794402 Medical literature:Randomized clinical trials .   No .
Reading Medical Literature reading medical literature;health statistics Journal Article Helping Doctors and Patients Make Sense of Health Statistics. https://www.ncbi.nlm.nih.gov/pubmed/?term=26161749 Reading Medical Literature on Health Statistics .   No .
Receiver Operating Characteristic Curve receiver operating characteristic curve Journal Article The meaning and use of the area under a receiver operating characteristic (ROC) curve. https://www.ncbi.nlm.nih.gov/pubmed/?term=7063747 ROC curves .   No .
Receiver Operating Characteristic Curves Receiver operating characteristic curves Journal Article A method of comparing the areas under receiver operating characteristic curves derived from the same cases. https://www.ncbi.nlm.nih.gov/pubmed/?term=6878708 ROC curves .   No .
Receiver Operating Characteristic Curves Receiver operating characteristic curves Journal Article Primer on certain elements of medical decision making. https://www.ncbi.nlm.nih.gov/pubmed/?term=806804 ROC curves .   No .
Receiver Operating Characteristic Curves receiver operating characteristic curves Journal Article How well do prediction equations predict? Using receiver operating characteristic curves and accuracy curves to compare validity and generalizability. https://www.ncbi.nlm.nih.gov/pubmed/?term=8347742 ROC curve .   No .
Receiver Operating Characteristic Curves Receiver operating characteristic curves Journal Article Efficient confidence bounds for ROC curves. https://www.ncbi.nlm.nih.gov/pubmed/?term=7973233 ROC curves .   No .
Receiver Operating Curve ROC; receiver operating curve Journal Article Statistics review 13: Receiver operating characteristic curves https://www.ncbi.nlm.nih.gov/pubmed/15566624 This review introduces some commonly used methods for assessing the performance of a diagnostic test. The sensitivity, specificity and likelihood ratio of a test are discussed. The uses of the receiver operating characteristic curve and the area under the curve are explained. 2 Any No 2
Recruitment recruitment;screening;RCTs Journal Article Recruitment of elderly volunteers for a multicenter clinical trial: the SHEP pilot study. https://www.ncbi.nlm.nih.gov/pubmed/?term=3743091 Recruitment/Screening in RCT's .   No .
Recruitment recruitment;screening;RCTs Journal Article Screening the elderly in the community: controlled trial of dependency surveillance using a questionnaire administered by volunteers. https://www.ncbi.nlm.nih.gov/pubmed/?term=2354297 Recruitment/Screening in RCT's .   No .
Recruitment recruitment;screening;RCTs Journal Article Systolic Hypertension in the Elderly Program (SHEP). Part 2: Screening and recruitment. https://www.ncbi.nlm.nih.gov/pubmed/?term=1999371 Recruitment/Screening in RCTs .   No .
Redcap REDCap;variable length;trim;$500 Website Trimming the length of REDCap variables https://www.pharmasug.org/proceedings/2012/CC/PharmaSUG-2012-CC17.pdf The default length of non-validated text variables in REDCap is $500. This tends to bloat the resulting SAS data set. There is a SAS macro in the referenced web page that sets the length of the text variables to the maximum value actually found in the data. 1 SAS No 2
Registries registries Journal Article Evaluation and implementation of public health registries. https://www.ncbi.nlm.nih.gov/pubmed/1902306 Registries .   No .
Registries Registries Journal Article Reliability of basic cancer patient data. https://www.ncbi.nlm.nih.gov/pubmed/?term=7187093 Registries .   No .
Registries Registries Journal Article Quality control practices in centralized tumor registries in North America. https://www.ncbi.nlm.nih.gov/pubmed/?term=2243256 Registries .   No .
Registries Registries Journal Article Coordinating data management for multiple ongoing clinical trials and registries. https://www.ncbi.nlm.nih.gov/pubmed/?term=10108493 Registries .   No .
Registries Registries Journal Article Estimation of Completeness of Cancer Registration https://www.ncbi.nlm.nih.gov/pubmed/?term=7152789 Registries .   No .
Registries Registries Journal Article A novel method of assessing completeness of tumor registration. https://www.ncbi.nlm.nih.gov/pubmed/6850515 Registries .   No .
Registries Registries Journal Article Approaches to quality control with an application to a new cancer registry in a developing country. https://www.ncbi.nlm.nih.gov/pubmed/?term=7722591 Registries .   No .
Registries Registries Journal Article Registry evaluation methods: a review and case study. https://www.ncbi.nlm.nih.gov/pubmed/?term=7000537 Registries .   No .
Registries Registries Journal Article Cost efficient sampling design for quality control of a birth defects registry. https://www.ncbi.nlm.nih.gov/pubmed/?term=1460474 Registries .   No .
Registries Registries Journal Article An application of capture-recapture methods to the estimation of completeness of cancer registration. https://www.ncbi.nlm.nih.gov/pubmed/?term=3367181 Registries .   No .
Regress multilevel;hierarchical;regress;data Journal Article Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures. https://www.ncbi.nlm.nih.gov/pubmed/?term=20949128 Multilevel/Hierarchical Regress .   No .
Regress multilevel;hierarchical;regress;data Journal Article Tutorial in biostatistics. An introduction to hierarchical linear modelling. https://www.ncbi.nlm.nih.gov/pubmed/?term=10327531 Multilevel / Hierarchical Regress .   No .
Regress multilevel;hierarchical;regress;data Journal Article Multi-level analysis in epidemiologic research on health behaviors and outcomes. https://www.ncbi.nlm.nih.gov/pubmed/1632420 Multilevel / Hierarchical Regress .   No .
Regression Regression Online Interactive Course Statistical Reasoning for Public Health 2: Regression Methods https://www.coursera.org/learn/statistical-reasoning-2?authMode=signup Per the course website, this non-degree course prepared by JHU provides a practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction. . Any Yes .
Regression Regression Online Interactive Course Statistical Reasoning for Public Health 2: Regression Methods https://www.coursera.org/learn/statistical-reasoning-2 Per the course website, this course prepared by JHU provides a practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction. . Any Yes .
Regression regression; model Journal Article A two-compartment regression model applied to compliance in a hypertension treatment program. https://www.ncbi.nlm.nih.gov/pubmed/?term=7410524 A two-compartment regression model applied to compliance in a hypertension treatment program. .   No .
Regression survival analysis;regression; Journal Article PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression. https://www.ncbi.nlm.nih.gov/pubmed/?term=25572709 We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time. We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling. .   No .
Regression regression;mean Journal Article Estimating the effect of regression toward the mean under stochastic censoring. https://www.ncbi.nlm.nih.gov/pubmed/?term=8109577 Regression to the Mean .   No .
Regression regression;mean Journal Article How much of the placebo 'effect' is really statistical regression? https://www.ncbi.nlm.nih.gov/pubmed/6369471 Regression to the Mean .   No .
Regression power sample size;regression;anova Journal Article Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system. https://www.ncbi.nlm.nih.gov/pubmed/?term=2507829 Power/sample size .   No .
Regression power sample size;regression Journal Article Robustness and power of analysis of covariance applied to data distorted from normality by floor effects: homogeneous regression slopes. https://www.ncbi.nlm.nih.gov/pubmed/?term=8668873 Power Sample size: regression .   No .
Regression power sample size;regression Journal Article Computing Confidence Bounds for Power and Sample Size of the General Linear Univariate Model. https://www.ncbi.nlm.nih.gov/pubmed/24039272 Power sample size: Regression .   No .
Regression power sample size;regression Journal Article A note on sample size computation for testing interactions. https://www.ncbi.nlm.nih.gov/pubmed/?term=3368673 Power sample size: Regression .   No .
Regression power sample size;regression Journal Article Power and sample size calculations for studies involving linear regression. https://www.ncbi.nlm.nih.gov/pubmed/9875838 Power Sample Size: Regression .   No .
Regression Analysis Linear Models; Multivariate Analysis; Regression Analysis Video Instruction Introduction to Model Selection https://www.youtube.com/watch?v=VB1qSwoF-l0&list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I This 5-minute video provides an overview to use of forward selection and backward elimination in developing a multivariable linear regression model. 2   No 1
Regression Models analysis;regression models;methods Journal Article An annotated bibliography of methods for analysing correlated categorical data. https://www.ncbi.nlm.nih.gov/pubmed/?term=1557577 This paper provides an annotated bibliography of over 100 articles concerning methods for analysing correlated categorical response data. Most of the papers listed here concern categorical regression models and estimation, with particular emphasis on binary responses. The papers are classified by several characteristics which group them according to common themes. The bibliography serves as a reference of methods for analysts of correlated categorical data, as well as for persons interested in methodologic work in this active area of statistical research. .   No .
Regression Models regression models; count models; censored and truncated regression; multivariate analysis; mixed effect models; power analysis Website UCLA Institute for Digital Research & Education Data Analysis Examples https://stats.idre.ucla.edu/other/dae/ This page contains links to examples illustrating the application of different statistical analysis techniques using different statistical packages. 4 SAS, SPSS, R, M+, No 3
Relative Risk relative risk; odds ratio; risk factor Journal Article Statistics Review 11: Assessing Risk https://www.ncbi.nlm.nih.gov/pubmed/15312212 Relative risk and odds ratio have been introduced in earlier reviews (see Statistics reviews 3, 6 and 8). This review describes the calculation and interpretation of their confidence intervals. The different circumstances in which the use of either the relative risk or odds ratio is appropriate and their relative merits are discussed. A method of measuring the impact of exposure to a risk factor is introduced. Measures of the success of a treatment using data from clinical trials are also considered. 2 Any No 2
Reliability statistical methods;data analysis;reliability Journal Article Biostatistics: how to detect, correct and prevent errors in the medical literature. https://www.ncbi.nlm.nih.gov/pubmed/?term=7349923 Approximately half the articles published in medical journals that use statistical methods use them incorrectly. These errors are so widespread that the present system of peer review has not been able to control them. This article presents a few rules of thumb to help readers identify questionable statistical analysis and estimate what the authors would have concluded had they used appropriate statistical methods. To prevent such errors from appearing, journals should secure review by someone knowledgeable in statistics before accepting a manuscript. In addition, human research committees should insist that an investigator define an appropriate strategy for data analysis before approving a protocol. Such policies should quickly and effectively increase the reliability of the clinical and scientific literature. .   No .
Reliability Reliability Journal Article Statistical methods for assessing agreement between two methods of clinical measurement. https://www.ncbi.nlm.nih.gov/pubmed/2868172 Reliability .   No .
Reliability Reliability Journal Article Intrarater reliability of manual muscle test (Medical Research Council scale) grades in Duchenne's muscular dystrophy. https://www.ncbi.nlm.nih.gov/pubmed/?term=1549632 Reliability .   No .
Reliability Reliability Journal Article A critical discussion of intraclass correlation coefficients. https://www.ncbi.nlm.nih.gov/pubmed/7701147 Reliability .   No .
Reliability reliability Journal Article Should echocardiography be performed to assess effects of antihypertensive therapy? Test-retest reliability of echocardiography for measurement of left ventricular mass and function. https://www.ncbi.nlm.nih.gov/pubmed/?term=7829797 reliability .   No .
Reliability reliability Journal Article Intraclass correlations: uses in assessing rater reliability. https://www.ncbi.nlm.nih.gov/pubmed/?term=18839484 reliability .   No .
Reliability Reliability Journal Article Intraclass correlations: uses in assessing rater reliability. https://www.ncbi.nlm.nih.gov/pubmed/?term=18839484 Reliability .   No .
Reliability Reliability Journal Article Reproducibility of the HERITAGE Family Study intervention protocol: drift over time. https://www.ncbi.nlm.nih.gov/pubmed/?term=9349912 Reliability .   No .
Reliability Reliability Journal Article The logistic modeling of interobserver agreement. https://www.ncbi.nlm.nih.gov/pubmed/1432004 Reliability .   No .
Reliability Reliability Journal Article The variability of serum cholesterol measurements: implications for screening and monitoring. https://www.ncbi.nlm.nih.gov/pubmed/?term=2384766 Reliability .   No .
Reliability Reliability Journal Article Evidence-based health policy--lessons from the Global Burden of Disease Study. https://www.ncbi.nlm.nih.gov/pubmed/?term=8966556 Reliability .   No .
Reliability Reliability;Ophthalmology Journal Article Reproducibility of visual acuity measurements in patients with retinitis pigmentosa. https://www.ncbi.nlm.nih.gov/pubmed/9051840 Reliability studies: Ophthalmology .   No .
Reliability reliability; ophthalmology Journal Article On the statistical reliability of letter-chart visual acuity measurements. https://www.ncbi.nlm.nih.gov/pubmed/8425819 Reliability studies: Ophthalmology .   No .
Reliability reliability; ophthalmology Journal Article Assessing the reliability, discriminative ability, and validity of disability glare tests. https://www.ncbi.nlm.nih.gov/pubmed/?term=8425818 Reliability studies: ophthalmology .   No .
Reliability reliability; ophthalmology Journal Article Validity and reliability of visual acuity measurements. https://www.ncbi.nlm.nih.gov/pubmed/?term=3253626 Reliability studies: ophthalmology .   No .
Reliability Reliability; ophthalmology Journal Article Measuring resolution in the contrast domain: the small letter contrast test. https://www.ncbi.nlm.nih.gov/pubmed/?term=8807651 Reliability studies: ophthalmology .   No .
Reliability Reliability;Ophthalmology Journal Article Reproducibility of anthropometric and body composition measurements: the HERITAGE Family Study. https://www.ncbi.nlm.nih.gov/pubmed/?term=9130027 Reliability studies: Ophthalmology .   No .
Reliability Reliability;ophthalmology Journal Article Comparison of generic versus disease-specific measures of functional impairment in patients with cataract. https://www.ncbi.nlm.nih.gov/pubmed/?term=7723440 Reliability studies: ophthalmology .   No .
Reliability Reliability;ophthalmology Journal Article Reproducibility of refraction and visual acuity measurement under a standard protocol. The Macular Photocoagulation Study Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=2480626 Reliability studies: ophthalmology .   No .
Reliability Reliability;ophthalmology Journal Article Reproducibility and responsiveness of the VF-14. An index of functional impairment in patients with cataracts. https://www.ncbi.nlm.nih.gov/pubmed/7487617 Reliability studies; ophthalmology .   No .
Reliability Reliability;Ophthalmology Journal Article The VF-14. An index of functional impairment in patients with cataract. https://www.ncbi.nlm.nih.gov/pubmed/8185520 Reliability studies: Ophthalmology .   No .
Reliability Reliability;ophthalmology Journal Article Interobserver reliability of the teller acuity card procedure in pediatric patients. https://www.ncbi.nlm.nih.gov/pubmed/8550321 Reliability studies; ophthalmology .   No .
Reliability Reliability;ophthalmology Journal Article The use of accurate visual acuity measurements in clinical anti-cataract formulation trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=3253632 Reliability studies: ophthalmology .   No .
Reliability power sample size;reliability Journal Article Sample size and optimal designs for reliability studies. https://www.ncbi.nlm.nih.gov/pubmed/9463853 Power Sample Size: Reliability Study .   No .
Reliability power sample size;reliability Journal Article Sample size requirements for reliability studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=3629046 Power Sample Size: Reliability Studies .   No .
Reliability Studies reliability studies;ophthalmology Journal Article Reliability of ophthalmic diagnoses in an epidemiologic survey. https://www.ncbi.nlm.nih.gov/pubmed/6881130 Reliability studies: ophthalmology .   No .
Repeated repeated;measures;analysis Journal Article Analysis of serial measurements in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2106931 repeated measures analysis .   No .
Repeated Measures repeated measures; multilevel linear models;repeated measures data; Journal Article Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. https://www.ncbi.nlm.nih.gov/pubmed/?term=23148474 Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression. .   No .
Repeated Measures clinical trials;analysis;repeated measures; confidence intervals Journal Article Statistical problems in the reporting of clinical trials. A survey of three medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=3614286 Reports of clinical trials often contain a wealth of data comparing treatments. This can lead to problems in interpretation, particularly when significance testing is used extensively. We examined 45 reports of comparative trials published in the British Medical Journal, the Lancet, or the New England Journal of Medicine to illustrate these statistical problems. The issues we considered included the analysis of multiple end points, the analysis of repeated measurements over time, subgroup analyses, trials of multiple treatments, and the overall number of significance tests in a trial report. Interpretation of large amounts of data is complicated by the common failure to specify in advance the intended size of a trial or statistical stopping rules for interim analyses. In addition, summaries or abstracts of trials tend to emphasize the more statistically significant end points. Overall, the reporting of clinical trials appears to be biased toward an exaggeration of treatment differences. Trials should have a clearer predefined policy for data analysis and reporting. In particular, a limited number of primary treatment comparisons should be specified in advance. The overuse of arbitrary significance levels (for example, P less than 0.05) is detrimental to good scientific reporting, and more emphasis should be given to the magnitude of treatment differences and to estimation methods such as confidence intervals. .   No .
Repeated Measures repeated measures;analysis Journal Article Analysis of serial measurements in medical research. https://www.ncbi.nlm.nih.gov/pubmed/?term=2106931 Repeated measures analysis .   No .
Repeated Measures repeated measures;analysis Journal Article Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design. https://www.ncbi.nlm.nih.gov/pubmed/1485053 Repeated measures analysis .   No .
Repeated Measures repeated measures;analysis Journal Article Modelling covariance structure in the analysis of repeated measures data. https://www.ncbi.nlm.nih.gov/pubmed/10861779 Repeated measures analysis .   No .
Repeated Measures power calculations;sample size;linear multivariate model;repeated measures Journal Article Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. https://www.ncbi.nlm.nih.gov/pubmed/?term=24790282 Power and sample size .   No .
Repeated Measures power sample size;repeated measures Journal Article Estimating sample sizes for repeated measurement designs. https://www.ncbi.nlm.nih.gov/pubmed/8205802 Power sample size: repeated measures .   No .
Repeated Measures Data mixed models; repeated measures data; cross-over trials, multi-center trials Book applied mixed models in medicine https://www.amazon.com/Applied-Models-Medicine-Statistics-Practice/dp/1118778251 very useful reference for analysis of longitudinal and correlated data using mixed models. good examples, SAS codes provided 4   No 4
Repeated Measures Data repeated measures; multilevel linear models;repeated measures data; Journal Article Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. https://www.ncbi.nlm.nih.gov/pubmed/?term=23148474 Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression. .   No .
Repeated Meaures power sample size;repeated meaures Journal Article Planning group sizes in clinical trials with a continuous outcome and repeated measures. https://www.ncbi.nlm.nih.gov/pubmed/?term=10070672 Power Sample Size: Repeated Measures .   No .
Resampling resampling; bootstrap; jackknife; cross-validation; simulation Website Don't Be Loopy: Re-Sampling and Simulation the SAS Way http://www2.sas.com/proceedings/forum2007/183-2007.pdf An excellent paper by David Cassell presented the SAS user group about how to program resampling statistics in SAS. The most common way that people do simulations and re-sampling plans in SAS® is, in fact, the slow and awkward way. People tend to think in terms of a huge macro loop wrapped around a piece of SAS code, with additional chunks of code to get the outputs of interest and then to weld together the pieces from each iteration. But SAS is designed to work with by-processing, so there is a better way. A faster way. This paper will show a simpler way to perform bootstrapping, jackknifing, cross-validation, and simulations from established populations. It is simpler and more efficient to get SAS to build all the iterations in one long SAS data set, then use by-processing to do all the computations at once. This lets us use SAS features to gather automatically the information from all the iterations, for simpler computations afterward. 4 SAS No 3
Research Design Publishing standards; Quality Control; Randomized Controlled Trials; Research Design; Therapeutic Equivalence; noninferiority trials; equivalence trials Journal Article Reporting of Noninferiority and Equivalence Randomized Trials: An Extension of the CONSORT Statement https://www.ncbi.nlm.nih.gov/pubmed/16522836 The CONSORT (Consolidated Standards of Reporting Trials) Statement, including a checklist and a flow diagram, was developed to help authors improve their reporting of randomized controlled trials. Its primary focus was on individually randomized trials with 2 parallel groups that assess the possible superiority of one treatment compared with another but is now being extended to other trial designs. Noninferiority and equivalence trials have methodological features that differ from superiority trials and present particular difficulties in design, conduct, analysis, and interpretation. Although the rationale for such trials occurs frequently, those designed and described specifically as noninferiority or equivalence trials appear less commonly in the medical literature. The quality of reporting of those that are published is often inadequate. In this article, we present an adapted CONSORT checklist for reporting noninferiority and equivalence trials and provide illustrative examples and explanations for those items amended from the original CONSORT checklist. The intent is to improve reporting of noninferiority and equivalence trials, enabling readers to assess the validity of their results and conclusions. 1   No 1
Research Design interrupted time series; research design Journal Article Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis https://www.ncbi.nlm.nih.gov/pubmed/26058820 Interrupted time series analysis is a quasi-experimental design that can evaluate an intervention e ect, using longitudinal data. The advantages, disadvantages, and underlying assumptions of various modelling approaches are discussed using published examples 3   No 2
Risk Factor relative risk; odds ratio; risk factor Journal Article Statistics Review 11: Assessing Risk https://www.ncbi.nlm.nih.gov/pubmed/15312212 Relative risk and odds ratio have been introduced in earlier reviews (see Statistics reviews 3, 6 and 8). This review describes the calculation and interpretation of their confidence intervals. The different circumstances in which the use of either the relative risk or odds ratio is appropriate and their relative merits are discussed. A method of measuring the impact of exposure to a risk factor is introduced. Measures of the success of a treatment using data from clinical trials are also considered. 2 Any No 2
Risk Factors Confounding Factors (Epidemiology); Data Interpretation, Statistical; Linear Models; Logistic Models; Multivariate Analysis; Proportional Hazards Models; Risk Factors Journal Article Katz MH. Multivariable Analysis: A Primer for Readers of Medical Research. Ann Intern Med 2003. https://www.ncbi.nlm.nih.gov/pubmed/12693887 An introduction to multivariable analysis, including linear regression and interaction terms. 1 Any No 1
Roc ROC; receiver operating curve Journal Article Statistics review 13: Receiver operating characteristic curves https://www.ncbi.nlm.nih.gov/pubmed/15566624 This review introduces some commonly used methods for assessing the performance of a diagnostic test. The sensitivity, specificity and likelihood ratio of a test are discussed. The uses of the receiver operating characteristic curve and the area under the curve are explained. 2 Any No 2
Roc diagnostic medicine; ROC; AUC; sample size calculation for diagnostic test Book Statistical Methods in Diagnostic Medicine https://www.amazon.com/Statistical-Methods-Diagnostic-Medicine-Xiao-Hua/dp/0470183144/ref=sr_1_1?s=books&ie=UTF8&qid=1515100984&sr=1-1&keywords=statistical+methods+in+diagnostic+medicine basic concepts and methods in diagnostic medicine such as ROC and AUC, estimation and hypothesis testing, sample size calculation for sensitivity, specificity, ROC and AUC, regression analysis for independent and correlated ROC data 6 Any Yes 4
Journal Article A note on interpreting an R² value   .   No .
Sample sample; population Journal Article Statistics review 2: Samples and populations https://www.ncbi.nlm.nih.gov/pubmed/11983040 The previous review in this series introduced the notion of data description and outlined some of the more common summary measures used to describe a dataset. However, a dataset is typically only of interest for the information it provides regarding the population from which it was drawn. The present review focuses on estimation of population values from a sample. 1   No .
Sample paired;community;designs;sample;size Journal Article Breaking the matches in a paired t-test for community interventions when the number of pairs is small. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481187 Paired Community Designs Sample Size .   No .
Sample paired;community;designs;sample;size Journal Article Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=2066748 Paired Community Designs Sample Size .   No .
Sample Size power; sample size Journal Article Statistics review 4: Sample size calculations https://www.ncbi.nlm.nih.gov/pubmed/12225610 The present review introduces the notion of statistical power and the hazard of under-powered studies. The problem of how to calculate an ideal sample size is also discussed within the context of factors that affect power, and specific methods for the calculation of sample size are presented for two common scenarios, along with extensions to the simplest case. 2   No 2
Sample Size power;sample size;software Software to Download G*Power http://www.gpower.hhu.de/ G*Power is a tool to compute statistical power analyses for many different t tests, F tests, chi-sq tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses. 4 Any, SAS, SPSS, R, No 2
Sample Size sample size; nonparametric tests Journal Article Sample Size Determination for Some Common Nonparametric Tests http://links.jstor.org/sici?sici=0162-1459%28198706%2982%3A398%3C645%3ASSDFSC%3E2.0.CO%3B2-V sample size calculation for common Nonparametric Tests like sign test, Wilcoxon one-sample and two-sample tests, Kendall's test. 4 Any No 4
Sample Size power; sample size; calculator; multivariate Interactive Program GLIMPPSE online Power and Sample Size Calculation http://glimmpse.samplesizeshop.org Well-documented online power calculator with guided steps. "GLIMMPSE can compute power or sample size for univariate and multivariate linear models with Gaussian errors." 59-page user manual is available at http://samplesizeshop.org/files/2012/08/GLIMMPSEUserManual_v2.0.0.pdf. 4   No 1
Sample Size effect size; power analysis; sample size Journal Article Measures of Clinical Significance https://www.ncbi.nlm.nih.gov/pubmed/14627890 This article outlines effect sizes for common situations in clinical research 2   No 1
Sample Size randomized clinical trials; sample size; algorithm;design; Journal Article A comprehensive algorithm for determining whether a run-in strategy will be a cost-effective design modification in a randomized clinical trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=8446807 In randomized clinical trials, poor compliance and treatment intolerance lead to reduced between-group differences, increased sample size requirements, and increased cost. A run-in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run-in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step-by-step algorithm through which one can estimate the cost-effectiveness of a potential run-in. Because the cost-effectiveness of a run-in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run-in on the required number of randomized, eligible, and screened subjects. Run-in strategies are most likely to be cost-effective when: (1) per patient costs during the post-randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run-in is inexpensive; (5) for most patients, the run-in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run-in are treatment intolerant or non-compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost-effectiveness of run-in strategies are stringent. In particular, if the only purpose of a run-in is to exclude ordinary partial compliers, the run-in will frequently add to the cost of the trial. Often, the cost-effectiveness of a run-in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects. .   No .
Sample Size design;sample size;power analysis; Journal Article Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification. https://www.ncbi.nlm.nih.gov/pubmed/?term=3567285%5Buid%5D When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates. .   No .
Sample Size cluster randomized trials;design;sample size Journal Article Methods for sample size determination in cluster randomized trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=26174515 The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. .   No .
Sample Size cluster randomization; sample size Journal Article Sample size calculations for cluster randomised controlled trials with a fixed number of clusters. https://www.ncbi.nlm.nih.gov/pubmed/?term=21718530 Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. .   No .
Sample Size sample size;clinical trials Journal Article Sample sizes for phase II and phase III clinical trials: an integrated approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=3787000 In this paper the following problem of clinical research is explored. Several potential new treatments are available for use against a certain disease. These are evaluated in a series of pilot studies which will constitute phase II clinical trials. The most promising will then be compared with a standard treatment in a phase III trial. Of interest will be the number of patients needed for the complete research programme, the proportions of these that should be involved in each phase, and the number of treatments which should be tried. Optimal strategies are found which maximize the probability that the overall programme identifies a treatment which is significantly better than the standard. . Stata No .
Sample Size sample size;clinical study;interpreting results Journal Article Sample size nomograms for interpreting negative clinical studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=6881780 In recent years there has been increasing attention to the appropriate interpretation of a clinical study. One special concern has been the difficulty inherent in interpreting studies that were not statistically significant: Was the sample size sufficient to detect a clinically important effect if, in fact, it existed? This concern is further complicated because readers may have differing opinions of what size effect is clinically important. A pair of sample size nomograms has been developed, using common levels of statistical significance, to assist in this interpretation. The nomograms are intended to provide the clinician with a handy and easy-to-use reference for ascertaining whether an apparently negative study has a sample size adequate to detect reliably any difference between treatment groups that the clinician believes is clinically important. Examples are provided to show these principles and the use of the nomograms in interpreting negative studies. .   No .
Sample Size sample size;randomization Journal Article Statistical power, sample size, and their reporting in randomized controlled trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8015121 To describe the pattern over time in the level of statistical power and the reporting of sample size calculations in published randomized controlled trials (RCTs) with negative results.Most trials with negative results did not have large enough sample sizes to detect a 25% or a 50% relative difference. This result has not changed over time. Few trials discussed whether the observed differences were clinically important. There are important reasons to change this practice. The reporting of statistical power and sample size also needs to be improved. .   No .
Sample Size clinical trials;confidence intervals;sample size Journal Article The case for confidence intervals in controlled clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=8001360 A statistical wit once remarked that researchers often pose the wrong question and then proceed to answer that question incorrectly. The question that researchers intend to ask is whether or not a treatment effect is clinically significant. The question that is typically asked, however, is whether or not the treatment effect is statistically significant--a question that may be only marginally related to the issue of clinical impact. Similarly, the response, in the form of a p value, is typically assumed to reflect clinical significance but in fact reflects statistical significance. In an attempt to address this problem the medical literature over the past decade has been moving away from tests of significance and toward the use of confidence intervals. Concretely, study reports are moving away from "the difference was significant with a p value under 0.01" and toward "the one-year survival rate was increased by 20 percentage points with a 95% confidence interval of 15 to 24 percentage points." By focusing on what the effect is rather than on what the effect is not confidence intervals offer an appropriate framework for reporting the results of clinical trials. This paper offers a non-technical introduction to confidence intervals, shows how the confidence intervals framework offers advantages over hypothesis testing, and highlights some of the controversy that has developed around the application of this method. Additionally, we make the argument that studies which will be reported in terms of confidence intervals should similarly be planned with reference to confidence intervals. The sample size should be set to ensure that the estimates of effect size will be reported not only with adequate power but also with appropriate precision. .   No .
Sample Size compliance;sample size;clinical trials Journal Article The effect of poor compliance and treatment side effects on sample size requirements in randomized clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7951277%5Buid%5D Treatment side effects and associated noncompliance have methodological implications vital to the testing of new drugs. In this paper, we quantify the impact of these factors on sample size requirements in clinical trials. In the Lipid Research Clinics Trial, side effects caused treatment group compliance (50.8%) to be lower than placebo compliance (67.3%). Cholesterol reduction among treatment noncompliers was 35.2% of the reduction among compliers. Had treatment group compliance been as high as placebo compliance, 41% fewer patients would have been required to achieve the same statistical power and an expected 31% more coronary events would have been prevented. We conclude: Because they discourage patient compliance, treatment side effects can (1) cause large sample size increases, (2) lead to underestimates of true efficacy, and (3) contribute to potentially invalid negative conclusions in clinical trials. The impact of side effects goes well beyond the complications and patient discomforts with which they are associated. .   No .
Sample Size randomized control trials;sample size;clinical trials Journal Article The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. Survey of 71 "negative" trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=355881 Seventy-one "negative" randomized control trials were re-examined to determine if the investigators had studied large enough samples to give a high probability (greater than 0.90) of detecting a 25 per cent and 50 per cent therapeutic improvement in the response. Sixty-seven of the trials had a greater than 10 per cent risk of missing a true 25 per cent therapeutic improvement, and with the same risk, 50 of the trials could have missed a 50 per cent improvement. Estimates of 90 per cent confidence intervals for the true improvement in each trial showed that in 57 of these "negative" trials, a potential 25 per cent improvement was possible, and 34 of the trials showed a potential 50 per cent improvement. Many of the therapies labeled as "no different from control" in trials using inadequate samples have not received a fair test. Concern for the probability of missing an important therapeutic improvement because of small sample sizes deserves more attention in the planning of clinical trials. .   No .
Sample Size power calculations;sample size;linear multivariate model;repeated measures Journal Article Power Calculations for General Linear Multivariate Models Including Repeated Measures Applications. https://www.ncbi.nlm.nih.gov/pubmed/?term=24790282 Power and sample size .   No .
Sample Size power;sample size;bioequivalence Journal Article Sample size determination in stratified trials to establish the equivalence of two treatments. https://www.ncbi.nlm.nih.gov/pubmed/?term=8677403 Power and Sample size Bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article Sample size requirements for evaluating a conservative therapy. https://www.ncbi.nlm.nih.gov/pubmed/?term=688245 Power and sample size: Bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article Comparison of tests and sample size formulae for proving therapeutic equivalence based on the difference of binomial probabilities. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481195 Power/sample size: bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article "Proving the null hypothesis" in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7160191 Power/sample size: bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article Sample size determination for proving equivalence based on the ratio of two means for normally distributed data. https://www.ncbi.nlm.nih.gov/pubmed/?term=9990695 Power and sample size: bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article Estimation and sample size considerations for clustered binary responses. https://www.ncbi.nlm.nih.gov/pubmed/?term=7973205 Power and sample size; bioequivalence .   No .
Sample Size power;sample size;bioequivalence Journal Article Sample size calculations for clustered binary data. https://www.ncbi.nlm.nih.gov/pubmed/?term=11427953 Power and sample size;bioequivalence .   No .
Sample Size power;sample size;compliance Journal Article Estimating the power of compliance-improving methods. https://www.ncbi.nlm.nih.gov/pubmed/?term=11146148 Power and sample size: compliance .   No .
Sample Size power;sample size;momma distributions Journal Article Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events. ACIP Investigators. Asymptomatic Cardiac Ischemia Pilot. https://www.ncbi.nlm.nih.gov/pubmed/?term=8047740 Power and sample size; Counts of events/Gomma Distributions .   No .
Sample Size power;sample size;contingency tables Journal Article Power evaluation of small drug and vaccine experiments with binary outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9463854 Power/Sample size: Contingency Tables .   No .
Sample Size power;sample size;contingency tables Journal Article Sample size determination based on Fisher's Exact Test for use in 2 x 2 comparative trials with low event rates. https://www.ncbi.nlm.nih.gov/pubmed/?term=1316828 Power/sample Size: contingency tables .   No .
Sample Size power;sample size;contengency tables Journal Article Power of testing proportions in small two-sample studies when sample sizes are equal. https://www.ncbi.nlm.nih.gov/pubmed/?term=8516594 Power/Sample size: Contingency Tables .   No .
Sample Size power;sample size;general Journal Article Introduction to sample size determination and power analysis for clinical trials https://www.ncbi.nlm.nih.gov/pubmed/?term=7273794 Power/Sample size: general .   No .
Sample Size power;sample size Journal Article Application of GEE procedures for sample size calculations in repeated measures experiments https://www.ncbi.nlm.nih.gov/pubmed/?term=9699236 Power/Sample size: general .   No .
Sample Size power;sample size Journal Article Approaches to sample size estimation in the design of clinical trials--a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8235182 Power/Sample size: general .   No .
Sample Size power;sample size Journal Article Planning and revising the sample size for a trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=7569499 Power/Sample size:general .   No .
Sample Size power;sample size Journal Article Sample-size calculation for a log-transformed outcome measure. https://www.ncbi.nlm.nih.gov/pubmed/?term=10588295 Power/Sample size:general .   No .
Sample Size power;sample size Journal Article The quest for "power": contradictory hypotheses and inflated sample sizes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9674660 Power/sample size: general .   No .
Sample Size power;sample size Journal Article Estimating sample size for epidemiologic studies: the impact of ignoring exposure measurement uncertainty. https://www.ncbi.nlm.nih.gov/pubmed/?term=9682326 Power/Sample size: general .   No .
Sample Size power;sample size Journal Article Design for sample size re-estimation with interim data for double-blind clinical trials with binary outcomes. https://www.ncbi.nlm.nih.gov/pubmed/?term=9304763 Power/sample size: general .   No .
Sample Size power;sample size Journal Article Additivity test for composition of binomial effects in chromosome aberrations after radiation injury. https://www.ncbi.nlm.nih.gov/pubmed/?term=8532987 Power/Sample size: general .   No .
Sample Size power;sample size Journal Article Sample size and power for prospective analysis of relative risk. https://www.ncbi.nlm.nih.gov/pubmed/?term=8511445 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Internal pilot studies for estimating sample size. https://www.ncbi.nlm.nih.gov/pubmed/7701146 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Power and sample size calculations for exact conditional tests with ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/?term=8369392 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Sample size determination for group sequential clinical trials with immediate response. https://www.ncbi.nlm.nih.gov/pubmed/?term=1518999 Power and sample size: general .   No .
Sample Size power;sample size Journal Article A criterion for the adequacy of a simple design when a complex model will be used for analysis. https://www.ncbi.nlm.nih.gov/pubmed/?term=1664791 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Sample size: clues, hints or suggestions. https://www.ncbi.nlm.nih.gov/pubmed/?term=4019709 Power and sample size; general .   No .
Sample Size power;sample size Journal Article How many patients are necessary to assess test performance? https://www.ncbi.nlm.nih.gov/pubmed/?term=2403604 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Adjusting sample size for anticipated dropouts in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=9564195 Power and sample size: general .   No .
Sample Size power;sample size Other Getting Ready to Estimate Sample Size: Hypotheses and Underlying Principles https://beckercat.wustl.edu/cgi-bin/koha/opac-detail.pl?biblionumber=29572 Power and sample size: general .   No .
Sample Size power;sample size Journal Article Sample izes for medical trials with special reference to long-term therapy. https://www.ncbi.nlm.nih.gov/pubmed/?term=5646357 Power and sample size: general .   No .
Sample Size power;sample size;ordered;categorical;data Journal Article Sample size calculations for ordered categorical data. https://www.ncbi.nlm.nih.gov/pubmed/8134732 Power/sample size: ordered categorical data .   No .
Sample Size paired;community;designs;sample size Journal Article Planning for the appropriate analysis in school-based drug-use prevention studies. https://www.ncbi.nlm.nih.gov/pubmed/?term=2212183 Paired Community Designs Sample Size .   No .
Sample Size paired;community;design;sample size Journal Article Aspects of statistical design for the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/?term=1315664 Paired Community Design Sample Size .   No .
Sample Size paired;community;designs;sample size Journal Article A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. https://www.ncbi.nlm.nih.gov/pubmed/?term=2084005 Paired Community Design Sample Size .   No .
Sample Size Calculation For Diagnostic Test diagnostic medicine; ROC; AUC; sample size calculation for diagnostic test Book Statistical Methods in Diagnostic Medicine https://www.amazon.com/Statistical-Methods-Diagnostic-Medicine-Xiao-Hua/dp/0470183144/ref=sr_1_1?s=books&ie=UTF8&qid=1515100984&sr=1-1&keywords=statistical+methods+in+diagnostic+medicine basic concepts and methods in diagnostic medicine such as ROC and AUC, estimation and hypothesis testing, sample size calculation for sensitivity, specificity, ROC and AUC, regression analysis for independent and correlated ROC data 6 Any Yes 4
Sas SAS; Graph Template Language; GTL; LIFETEST Video Instruction Introduction to Graph Template Language (GTL) https://www.youtube.com/watch?v=Tu8jgNgLgfI Youtube video with an introduction to SAS Graph Template Language (GTL). GTL programming allows a user to customize the ODS graphics from SAS procedures. It is particularly useful for customizing the survival plots from LIFETEST. 4 SAS No 3
Sas SAS; SAS Tutorials; SAS programming; SAS studio; Online Interactive Course SAS tutorials http://video.sas.com/#category/videos/how-to-tutorials How To Use SAS Tutorials . SAS No .
Sas SAS; SAS macro; SAS modeling Online Interactive Course SAS e-Learning https://support.sas.com/edu/elearning.html?ctry=us&productType=library Excellent tool to learn the utilization of SAS. 4 SAS Yes .
Sas SAS; SAS macro; PROC SQL; SAS statistics Interactive Program Data Science with SAS Certification Training https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-excel-training Gain an understanding of SAS, statistics, PROC SQL, SAS macros 4 SAS Yes .
Sas SAS; SAS programming; SAS basic Online Interactive Course SAS programming for beginners https://www.udemy.com/sas-programming-for-beginners/ Helps users get basic knowledge of SAS language and programming skills, designed for beginners, learning SAS step-by-step with hands-on SAS programs . SAS Yes .
Sas SAS; SAS programming Online Interactive Course SAS Programming https://extension.ucsd.edu/courses-and-programs/sas-programming Per website, this course will help: - Importing, exporting, manipulating, transforming, and combining data - Create reports using SAS procedures - Advanced DATA step programming techniques - Using PROC SQL in SAS - Creating and using SAS Macros - Use ODS to create data visualizations and output in multiple formats . SAS No .
Sas SAS; SAS certification Online Interactive Course Analytics Using SAS® https://www.capella.edu/online-degrees/certificate-analytics-sas/#/readMore SAS® Certification and a Digital Badge during Your Program . SAS Yes .
Sas SAS; SAS online training; SAS programming Online Interactive Course Online Certificate in Applied Statistics using SAS http://csm.kennesaw.edu/statistics/programs/certificate-applied-statistics-sas-online.php This online program is an integrated extension of the highly respected traditional curriculum in Applied Statistics at Kennesaw State University (KSU). . SAS Yes .
Sas SAS; SAS tutorials; SAS beginners Online Interactive Course SAS Training and Tutorials https://www.lynda.com/SAS-tutorials/8038-0.html SAS for beginners . SAS Yes .
Sas SAS; SAS programming Website SAS Class Notes https://stats.idre.ucla.edu/sas/seminars/notes/ The class notes are not meant to be a SAS textbook or a reference manual. However, it is possible for individuals to use the class notes to help in learning SAS even if they don't enroll in the classes. - Entering Data - Exploring Data - Modifying Data - Managing Data - Analyzing Data - General Information . SAS No .
Sas SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Sas Basic SAS; SAS programming; SAS basic Online Interactive Course SAS programming for beginners https://www.udemy.com/sas-programming-for-beginners/ Helps users get basic knowledge of SAS language and programming skills, designed for beginners, learning SAS step-by-step with hands-on SAS programs . SAS Yes .
Sas Beginners SAS; SAS tutorials; SAS beginners Online Interactive Course SAS Training and Tutorials https://www.lynda.com/SAS-tutorials/8038-0.html SAS for beginners . SAS Yes .
Sas Certification SAS; SAS certification Online Interactive Course Analytics Using SAS® https://www.capella.edu/online-degrees/certificate-analytics-sas/#/readMore SAS® Certification and a Digital Badge during Your Program . SAS Yes .
Sas Code SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Sas Macro SAS; SAS macro; SAS modeling Online Interactive Course SAS e-Learning https://support.sas.com/edu/elearning.html?ctry=us&productType=library Excellent tool to learn the utilization of SAS. 4 SAS Yes .
Sas Macro SAS; SAS macro; PROC SQL; SAS statistics Interactive Program Data Science with SAS Certification Training https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-excel-training Gain an understanding of SAS, statistics, PROC SQL, SAS macros 4 SAS Yes .
Sas Modeling SAS; SAS macro; SAS modeling Online Interactive Course SAS e-Learning https://support.sas.com/edu/elearning.html?ctry=us&productType=library Excellent tool to learn the utilization of SAS. 4 SAS Yes .
Sas Online Training SAS; SAS online training; SAS programming Online Interactive Course Online Certificate in Applied Statistics using SAS http://csm.kennesaw.edu/statistics/programs/certificate-applied-statistics-sas-online.php This online program is an integrated extension of the highly respected traditional curriculum in Applied Statistics at Kennesaw State University (KSU). . SAS Yes .
Sas Programming SAS; SAS Tutorials; SAS programming; SAS studio; Online Interactive Course SAS tutorials http://video.sas.com/#category/videos/how-to-tutorials How To Use SAS Tutorials . SAS No .
Sas Programming SAS; SAS programming; SAS basic Online Interactive Course SAS programming for beginners https://www.udemy.com/sas-programming-for-beginners/ Helps users get basic knowledge of SAS language and programming skills, designed for beginners, learning SAS step-by-step with hands-on SAS programs . SAS Yes .
Sas Programming SAS; SAS programming Online Interactive Course SAS Programming https://extension.ucsd.edu/courses-and-programs/sas-programming Per website, this course will help: - Importing, exporting, manipulating, transforming, and combining data - Create reports using SAS procedures - Advanced DATA step programming techniques - Using PROC SQL in SAS - Creating and using SAS Macros - Use ODS to create data visualizations and output in multiple formats . SAS No .
Sas Programming SAS; SAS online training; SAS programming Online Interactive Course Online Certificate in Applied Statistics using SAS http://csm.kennesaw.edu/statistics/programs/certificate-applied-statistics-sas-online.php This online program is an integrated extension of the highly respected traditional curriculum in Applied Statistics at Kennesaw State University (KSU). . SAS Yes .
Sas Programming SAS; SAS programming Website SAS Class Notes https://stats.idre.ucla.edu/sas/seminars/notes/ The class notes are not meant to be a SAS textbook or a reference manual. However, it is possible for individuals to use the class notes to help in learning SAS even if they don't enroll in the classes. - Entering Data - Exploring Data - Modifying Data - Managing Data - Analyzing Data - General Information . SAS No .
Sas Statistics SAS; SAS macro; PROC SQL; SAS statistics Interactive Program Data Science with SAS Certification Training https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-excel-training Gain an understanding of SAS, statistics, PROC SQL, SAS macros 4 SAS Yes .
Sas Studio SAS; SAS Tutorials; SAS programming; SAS studio; Online Interactive Course SAS tutorials http://video.sas.com/#category/videos/how-to-tutorials How To Use SAS Tutorials . SAS No .
Sas Studio SAS tutorials; Enterprise Guide; SAS Studio;Viya Video Instruction Portal to 200 SAS Video Tutorials http://video.sas.com/#category/videos/how-to-tutorials Video tutorials on a wide variety of SAS topics, from programming to statistics. Many use SAS Studio or Enterprise Guide. 1 SAS No 1
Sas Tutorials SAS; SAS Tutorials; SAS programming; SAS studio; Online Interactive Course SAS tutorials http://video.sas.com/#category/videos/how-to-tutorials How To Use SAS Tutorials . SAS No .
Sas Tutorials SAS; SAS tutorials; SAS beginners Online Interactive Course SAS Training and Tutorials https://www.lynda.com/SAS-tutorials/8038-0.html SAS for beginners . SAS Yes .
Sas Tutorials SAS tutorials; Enterprise Guide; SAS Studio;Viya Video Instruction Portal to 200 SAS Video Tutorials http://video.sas.com/#category/videos/how-to-tutorials Video tutorials on a wide variety of SAS topics, from programming to statistics. Many use SAS Studio or Enterprise Guide. 1 SAS No 1
Screening recruitment;screening;RCTs Journal Article Recruitment of elderly volunteers for a multicenter clinical trial: the SHEP pilot study. https://www.ncbi.nlm.nih.gov/pubmed/?term=3743091 Recruitment/Screening in RCT's .   No .
Screening recruitment;screening;RCTs Journal Article Screening the elderly in the community: controlled trial of dependency surveillance using a questionnaire administered by volunteers. https://www.ncbi.nlm.nih.gov/pubmed/?term=2354297 Recruitment/Screening in RCT's .   No .
Screening recruitment;screening;RCTs Journal Article Systolic Hypertension in the Elderly Program (SHEP). Part 2: Screening and recruitment. https://www.ncbi.nlm.nih.gov/pubmed/?term=1999371 Recruitment/Screening in RCTs .   No .
Sequential group;sequential;tests Journal Article A multiple testing procedure for clinical trials https://www.ncbi.nlm.nih.gov/pubmed/497341 Group Sequential Tests .   No .
Sequential group;sequential;tests Journal Article An aid to data monitoring in long-term clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=7160189 Group Sequential Tests .   No .
Sequential group;sequential;test Journal Article Interim analyses in randomized clinical trials: ramifications and guidelines for practitioners. https://www.ncbi.nlm.nih.gov/pubmed/?term=3567306 Group Sequential Tests .   No .
Sequential group;sequential;tests Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Tests .   No .
Sequential group;sequential;test Journal Article Some extensions to a new approach for interim analysis in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1876784 Group Sequential Tests .   No .
Sequential group;sequential;test Journal Article Interim analysis in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1876782 Group Sequential Tests .   No .
Sequential group;sequential;test;survival analysis;data Other Stochastically Curtailed tests in Long-term Clinical Trials from Long Term Clinical Trials https://www.tandfonline.com/doi/abs/10.1080/07474948208836014 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article Statistics: the problem of examining accumulating data more than once. https://www.ncbi.nlm.nih.gov/pubmed/?term=4589874 Group Sequential Tests .   No .
Sequential group;sequential;tests;data;clinical trials Journal Article On the choice of times for data analysis in group sequential clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/1912268 Group Sequential Test .   No .
Sequential group;sequential;tests;design;analysis;data Journal Article Group sequential methods in the design and analysis of clinical trials https://www.jstor.org/stable/2335684?seq=1#metadata_info_tab_contents Group Sequential Tests .   No .
Sequential group;sequential;tests;clinical trials;data Journal Article Group sequential testing in clinical trials with multivariate observations: a review. https://www.ncbi.nlm.nih.gov/pubmed/?term=8122047 Group Sequential Tests .   No .
Sequential group;sequential;tests Book Biopharmaceutical Statistics for Drug Development   Groups Sequential Tests .   No .
Sequential group;sequential;test;data Journal Article Monitoring Clinical Trial Data for Evidence of Adverse or Beneficial Treatment Effects. P.L. Canner   Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article Discrete sequential boundaries for clinical trials https://academic.oup.com/biomet/article/70/3/659/247777 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article On choosing the number of interim analyses in clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7187080 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article One-sample multiple testing procedure for phase II clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/7082756 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article Can early stopping procedures impact significantly on the efficiency of clinical trials without serious loss of information? https://www.ncbi.nlm.nih.gov/pubmed/?term=6528138 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article Symmetric group sequential test designs. https://www.ncbi.nlm.nih.gov/pubmed/?term=2675998 Group Sequential Tests .   No .
Sequential group;sequential;tests;data Journal Article Monitoring treatment differences in long-term clinical trials. https://www.ncbi.nlm.nih.gov/pubmed/?term=588655 Group Sequential Tests .   No .
Sharing Data Data Management; sharing data Online Interactive Course Research Data Management and Sharing https://www.coursera.org/learn/data-management Per the course website, this course was prepared by the University of North Carolina at Chapel Hill and the University of Edinburgh to provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories. . Any Yes .
Sign Test sign test; Wilcoxon signed rank test; Wilcoxon rank sum test; Mann-Whitney test; nonparametric tests Journal Article Statistics review 6: Nonparametric methods https://www.ncbi.nlm.nih.gov/pubmed/12493072 The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed. 2   No .
Significance hypothesis testing; significance; P value Journal Article Statistics review 3: Hypothesis testing and P values https://www.ncbi.nlm.nih.gov/pubmed/12133182 The present review introduces the general philosophy behind hypothesis (significance) testing and calculation of P values. Guidelines for the interpretation of P values are also provided in the context of a published example, along with some of the common pitfalls. Examples of specific statistical tests will be covered in future reviews. 2   No .
Significance significance Journal Article Confidence intervals assess both clinical significance and statistical significance. https://www.ncbi.nlm.nih.gov/pubmed/?term=1994799 Confidence intervals assess both clinical significance and statistical significance. .   No .
Simulation resampling; bootstrap; jackknife; cross-validation; simulation Website Don't Be Loopy: Re-Sampling and Simulation the SAS Way http://www2.sas.com/proceedings/forum2007/183-2007.pdf An excellent paper by David Cassell presented the SAS user group about how to program resampling statistics in SAS. The most common way that people do simulations and re-sampling plans in SAS® is, in fact, the slow and awkward way. People tend to think in terms of a huge macro loop wrapped around a piece of SAS code, with additional chunks of code to get the outputs of interest and then to weld together the pieces from each iteration. But SAS is designed to work with by-processing, so there is a better way. A faster way. This paper will show a simpler way to perform bootstrapping, jackknifing, cross-validation, and simulations from established populations. It is simpler and more efficient to get SAS to build all the iterations in one long SAS data set, then use by-processing to do all the computations at once. This lets us use SAS features to gather automatically the information from all the iterations, for simpler computations afterward. 4 SAS No 3
Size paired;community;designs;sample;size Journal Article Breaking the matches in a paired t-test for community interventions when the number of pairs is small. https://www.ncbi.nlm.nih.gov/pubmed/?term=7481187 Paired Community Designs Sample Size .   No .
Size paired;community;designs;sample;size Journal Article Data analysis and sample size issues in evaluations of community-based health promotion and disease prevention programs: a mixed-model analysis of variance approach. https://www.ncbi.nlm.nih.gov/pubmed/?term=2066748 Paired Community Designs Sample Size .   No .
Skewed Data distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Software power;sample size;software Software to Download G*Power http://www.gpower.hhu.de/ G*Power is a tool to compute statistical power analyses for many different t tests, F tests, chi-sq tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses. 4 Any, SAS, SPSS, R, No 2
Software Development R; software development Online Interactive Course Mastering Software Development in R Specialization https://www.coursera.org/specializations/r Per the course website, this is a 5-course specialiation prepared by JHU. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. . R Yes .
Specific Analysis Not Cardiology MetaAnalysis;Specific Analysis Not Cardiology Journal Article Thiazide diuretic agents and the incidence of hip fracture. https://www.ncbi.nlm.nih.gov/pubmed/?term=2296269 MetaAnalysis-Specific Analysis Not Cardiology .   No .
Sps Programming SPSS; IBM; SPS programming Online Interactive Course IBM SPSS Statistics (Local) - Role: New User https://www-03.ibm.com/services/learning/ites.wss/zz/en/?pageType=page&c=V156959C29019C95 Pathways to courses provided by IBM to learn SPSS . SPSS No .
Spss SPSS; IBM; SPS programming Online Interactive Course IBM SPSS Statistics (Local) - Role: New User https://www-03.ibm.com/services/learning/ites.wss/zz/en/?pageType=page&c=V156959C29019C95 Pathways to courses provided by IBM to learn SPSS . SPSS No .
Spss SPSS; SPSS proramming Online Interactive Course SPSS Training and Tutorials https://www.lynda.com/SPSS-training-tutorials/1009-0.html Learning basic SPSS . SPSS Yes .
Spss SPSS; SPSS programming Online Interactive Course Introduction to SPSS https://onlinecourses.science.psu.edu/statprogram/spss Introduction to data entry, statistical analysis, modeling and graphing in SPSS . SPSS No .
Spss SPSS; programming Online Interactive Course Getting started with SPSS http://www.open.edu/openlearn/people-politics-law/politics-policy-people/sociology/getting-started-spss/content-section-0?active-tab=description-tab Step-by-step approach to statistics software through seven interactive activities . SPSS No .
Spss SPSS; programming Online Interactive Course SPSS https://stats.idre.ucla.edu/spss/ Tips to learn SPSS . SPSS No .
Spss SPSS; programming Website SPSS for windows http://www.psych.utoronto.ca/courses/c1/spss/page1.htm SPSS tutorial for windows, learning SPSS basics . SPSS No .
Spss SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
Spss Programming SPSS; SPSS programming Online Interactive Course Introduction to SPSS https://onlinecourses.science.psu.edu/statprogram/spss Introduction to data entry, statistical analysis, modeling and graphing in SPSS . SPSS No .
Spss Proramming SPSS; SPSS proramming Online Interactive Course SPSS Training and Tutorials https://www.lynda.com/SPSS-training-tutorials/1009-0.html Learning basic SPSS . SPSS Yes .
Sql SQL Online Interactive Course SQL for Data Science https://www.coursera.org/learn/sql-for-data-science Per the course website, this course prepared by the UC Davis is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. . Any Yes .
Sql SQL; NoSQL Online Interactive Course Data Science at Scale Specialization https://www.coursera.org/specializations/data-science Per the course website, this 4-day course prepared by the University of Washington covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you . Any Yes .
Sql Data Science; SQL; importing and cleaning data; time series; machine learning; Online Interactive Course DataCamp - courses for Data Science https://www.datacamp.com/home This provides high quality courses on Data Science at both an introductory and advanced level using either R or Python. Some courses are free, others require a monthly fee to access. 1 R, Other Yes 2
Standard Deviation distribution of data; outliers; presentation of data; measures of variability; range, interquartile range; standard deviation; measures of location; mean; median; mode; data transformations; skewed data Journal Article Statistics review 1: Presenting and summarising data https://www.ncbi.nlm.nih.gov/pubmed/11940268 The present review is the first in an ongoing guide to medical statistics, using specific examples from intensive care. The first step in any analysis is to describe and summarize the data. As well as becoming familiar with the data, this is also an opportunity to look for unusually high or low values (outliers), to check the assumptions required for statistical tests, and to decide the best way to categorize the data if this is necessary. In addition to tables and graphs, summary values are a convenient way to summarize large amounts of information. This review introduces some of these measures. It describes and gives examples of qualitative data (unordered and ordered) and quantitative data (discrete and continuous); how these types of data can be represented figuratively; the two important features of a quantitative dataset (location and variability); the measures of location (mean, median and mode); the measures of variability (range, interquartile range, standard deviation and variance); common distributions of clinical data; and simple transformations of positively skewed data. 1   No .
Stata SAS; R; STATA; SPSS; M+; data analysis examples; SAS code; SAS macros; choose correct statistical tests Website UCLA Institute for Digital Research and Education https://stats.idre.ucla.edu/ Lots of resources like software (SAS, R, STATA, SPSS, M+), data analysis examples, codes and output, SAS macros, how to choose correct statistical tests, etc. 4 SAS, SPSS, R, M+, No 4
State-Of-The-Science Meta-analysis;State-of-the-Science Journal Article Meta-analysis: State-of-the-Science https://www.ncbi.nlm.nih.gov/pubmed/?term=7977286 Meta-analysis: State-of-the-Science .   No .
Statistical nonparometric;statistical;methods Journal Article On the efficacy of the rank transformation in stepwise logistic and discriminant analysis. https://www.ncbi.nlm.nih.gov/pubmed/8446809 Nonparometric Statistical Methods .   No .
Statistical non parametric;statistical;methods Journal Article Should we always choose a nonparametric test when comparing two apparently nonnormal distributions? https://www.ncbi.nlm.nih.gov/pubmed/?term=11165471 Non parametric Statistical Methods .   No .
Statistical Computing IML; matrix programing; statistical computing Website The DO Loop blog http://blogs.sas.com/content/iml/ Rick Wicklin is a researcher in computational statistics at SAS and is a principal developer of PROC IML and SAS/IML Studio. This blog focuses on statistical programming. It discusses statistical and computational algorithms, statistical graphics, simulation, efficiency, and data analysis. 4 SAS No 4
Statistical Inference Statistical Inference Online Interactive Course Statistical Inference https://www.coursera.org/learn/statistical-inference Per the course website, this course is prepared by JHU. Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, . Any Yes .
Statistical Learning Statistical Learning Video Instruction Statistical Learning Self Paced Course https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about The active course run for Statistical Learning has ended, but the course is now available in a self paced mode. You are welcome to join the course and work through the material and exercises at your own pace. When you have completed the exercises with a score of 50% or higher, you can generate your Statement of Accomplishment from within the course. The course will remain available for an extended period of time. We anticipate the content will be available until at least December 31, 2020. You will be notified by email of any changes to content availability beforehand. 4 R No 4
Statistical Learning Methods Statistical learning methods Online Interactive Course Statistical Learning with Applications in R https://www.youtube.com/watch?v=3jQs02dbfrI&list=PL06ytJZ4Ak1rXmlvxTyAdOEfiVEzH00IK Reference: (Book) (Chapter 2) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) http://www-bcf.usc.edu/~gareth/ISL/IS... 4 R No 4
Statistical Methods statistical methods;data analysis;reliability Journal Article Biostatistics: how to detect, correct and prevent errors in the medical literature. https://www.ncbi.nlm.nih.gov/pubmed/?term=7349923 Approximately half the articles published in medical journals that use statistical methods use them incorrectly. These errors are so widespread that the present system of peer review has not been able to control them. This article presents a few rules of thumb to help readers identify questionable statistical analysis and estimate what the authors would have concluded had they used appropriate statistical methods. To prevent such errors from appearing, journals should secure review by someone knowledgeable in statistics before accepting a manuscript. In addition, human research committees should insist that an investigator define an appropriate strategy for data analysis before approving a protocol. Such policies should quickly and effectively increase the reliability of the clinical and scientific literature. .   No .
Statistical Significance statistical significance; meta-analysis Journal Article Is everything we eat associated with cancer? A systematic cookbook review. https://www.ncbi.nlm.nih.gov/pubmed/?term=23193004 statistical significance; meta-analysis .   No .
Statistical Significance statistical significance; randomized efficacy; trials Journal Article Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. https://www.ncbi.nlm.nih.gov/pubmed/9450711 statistical significance, randomized efficacy .   No .
Statistics genomics; statistics Online Interactive Course Genomics: the connection to public health practice http://cpheo1.sph.umn.edu/genomics/ Per the course website, the module will help to: Describe the overall knowledge of genomics and the applications of genomics in the public health field. Identify and access genomic resources and tools, including academic partnerships for public health practice. Identify practice opportunities for integrating genomics into public health practice and research. Discuss ethical, legal, social, and policy issues surrounding genomics and public health. Discuss the cultural challenges and varying perceptions of the value of genomics in the public health field. . Other No .
Statistics Statistics; R Online Interactive Course Statistics with R Specialization https://www.coursera.org/specializations/statistics Per the course website, this non degree course offered by Duke University helps to learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. . R Yes .
Statistics Probability; Statistics Online Interactive Course Introduction to Probability and Data https://www.coursera.org/learn/probability-intro Per the course website, this non degree course offered by Duke University, introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization. . Any Yes .
Statistics Math; Statistics Online Interactive Course Data Science Math Skills https://www.coursera.org/learn/datasciencemathskills Per the course website, learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes . Any Yes .
Statistics R Capstone; Statistics Online Interactive Course Statistics with R Capstone https://www.coursera.org/learn/statistics-project Per the course website, the capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. . Any Yes .
Statistics Informatics; Statistics Online Interactive Course Materials Data Sciences and Informatics https://www.coursera.org/learn/material-informatics Per the course website, this course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges. . Any Yes .
Statistics Statistics Online Interactive Course Data Science Specialization https://www.coursera.org/specializations/jhu-data-science Per the course website, this 10-course specialiation prepared by JHU to learn how to ask the right questions, manipulate data sets, and create visualizations to communicate results. . Any Yes .
Statistics Statistics; Genomics Online Interactive Course Statistics for Genomic Data Science https://www.coursera.org/learn/statistical-genomics Per the course website, this is course 7 of 8 in the Genomic Data Science Specialization prepared by JHU as an introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University. . Any Yes .
Statistics Estimation; Inference; Statistics Online Interactive Course Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation https://www.coursera.org/learn/statistical-reasoning-1 Per the course website, this course, prepared by JHU, provides a conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics. . Any Yes .
Statistics Linear Models; Statistics Online Interactive Course Advanced Linear Models for Data Science 2: Statistical Linear Models https://www.coursera.org/learn/linear-models-2 Per the course website, this course, prepared by JHU, helps to provide a basic understanding of linear algebra and multivariate calculus, a basic understanding of statistics and regression models, at least a little familiarity with proof based mathematics, and basic knowledge of the R programming language. . Any Yes .
Statistics Statistics; analysis; summaries Online Interactive Course Exploratory Data Analysis https://www.coursera.org/learn/exploratory-data-analysis Per the course website, this is course 4 of 10 in the Data Science Specialization. It is prepared by JHU and helps cover the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. . Any Yes .
Statistics Least Squares; Data modeling; Statistics Online Interactive Course Advanced Linear Models for Data Science 1: Least Squares https://www.coursera.org/learn/linear-models Per the course website, this course is prepared by JHU to help provide: A basic understanding of linear algebra and multivariate calculus; A basic understanding of statistics and regression models; At least a little familiarity with proof based mathematics; and Basic knowledge of the R programming language. . Any Yes .
Statistics Team; Statistics Online Interactive Course Building a Data Science Team https://www.coursera.org/learn/build-data-science-team Per the course website, this course prepared by JHU helps learn the following: 1. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams . Any Yes .
Statistics Statistics; analysis tools Online Interactive Course Understanding Your Data: Analytical Tools https://www.coursera.org/learn/uva-darden-understanding-data-tools Per the course website, this course is prepared by the University of Virginia. By the end of this course, you will understand the differences between mediation and moderation and between moderated mediation and mediated moderation models (conditional indirect effects), and the importance of multilevel analysis. Most important, you will be able to run mediation, moderation, conditional indirect effect and multilevel models and interpret the results. . SPSS No .
Statistics ethics;statistics Journal Article Statistics in medical journals. https://www.ncbi.nlm.nih.gov/pubmed/?term=7187083 The general standard of statistics in medical journals is poor. This paper considers the reasons for this with illustrations of the types of error that are common. The consequences of incorrect statistics in published papers are discussed; these involve scientific and ethical issues. Suggestions are made about ways in which the standard of statistics may be improved. Particular emphasis is given to the necessity for medical journals to have proper statistical refereeing of submitted papers. .   No .
Structural Equation Models Graphics; Linear Models; Structural Equation Models; Categorical Models Website Michael Friendly's personal website http://www.datavis.ca/courses/index.php A variety resources and code for producing graphs and analyzing data 3 SAS, R No 3
Strut Fracture medical practice problems;strut fracture;Bjork-Shiley; Journal Article Risk of strut fracture of Björk-Shiley valves. https://www.ncbi.nlm.nih.gov/pubmed/1346279 Medical practice problems .   No .
Studies matching;community based;studies Journal Article The efficiency of the matched-pairs design of the Community Intervention Trial for Smoking Cessation (COMMIT). https://www.ncbi.nlm.nih.gov/pubmed/9129857 Matching Community Based Studies .   No .
Studies matching;community based;studies;case control Journal Article The merits of matching in community intervention trials: a cautionary tale. https://www.ncbi.nlm.nih.gov/pubmed/?term=9265698 Matching Community Based Studies .   No .
Studies matching;community based;studies;case control studies Journal Article The effect of matching on the power of randomized community intervention studies. https://www.ncbi.nlm.nih.gov/pubmed/8456215 Matching Community Based Studies .   No .
Study Design study design Journal Article Ensuring data quality in medical research through an integrated data management system. https://www.ncbi.nlm.nih.gov/pubmed/?term=6463448 An effective data management system ensures high quality research data by making certain of the proper execution of the study design. This paper presents the components of a data management system and describes procedures for use in each component of the system to obtain high quality data. We discuss the interrelationship among the components of the data management system and the relationship of the data management system to other parts of the research project. We identify underlying principles in design and implementation of a data management system to ensure high quality data. .   No .
Study Design randomized clinical trial;study design Journal Article Rationale and design of a randomized clinical trial on prevention of stroke in isolated systolic hypertension. The Systolic Hypertension in the Elderly Program (SHEP) Cooperative Research Group. https://www.ncbi.nlm.nih.gov/pubmed/?term=2905387 Isolated systolic hypertension (ISH)--i.e. high systolic pressure with nonhypertensive (less than 90 mmHg) diastolic pressure--is a recognized risk factor for cardiovascular disease among individuals in the age group 60 years and above. This observation suggests that antihypertensive treatment might be beneficial. Results of the Systolic Hypertension in the Elderly Program Pilot Study (SHEP-PS) indicated the feasibility of a full-scale clinical trial on the efficacy of drug treatment of ISH. The Systolic Hypertension in the Elderly Program (SHEP) is a randomized, double-blind, placebo-controlled clinical trial with the primary objective of assessing the effect of drug treatment of ISH--systolic pressure 160-219 mmHg and diastolic pressure less than 90--on occurrence of fatal and nonfatal stroke. This multicenter clinical trial has a sample size of 4736 participants, with high statistical power to detect a reduction of 32% or more in the study's primary end point during the 4-6 year period of treatment and follow-up. Low dosage chlorthalidone is the main study drug. Further features of the design of SHEP and the trial's organization are described. .   No .
Study Design interpretation; bias;study design Journal Article Why most published research findings are false. https://www.ncbi.nlm.nih.gov/pubmed/16060722 Interpretation, bias, study design .   No .
Study Design longitudinal;study design;data analysis;linear models;laird;ware Journal Article Families of lines: random effects in linear regression analysis. https://www.ncbi.nlm.nih.gov/pubmed/3379003 Longitudinal Studies Design: data analysis: linear (Laird/Ware) Models .   No .
Study Design Longitudinal;Study Design;data analysis;linear Models;Laird;Ware Journal Article The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. https://www.ncbi.nlm.nih.gov/pubmed/?term=3526500 Longitudinal Studies Design;data analysis;linear (Laird/Ware) Models .   No .
Study Design longitudinal;study design Journal Article Planning a longitudinal study. II. Frequency of measurement and study duration. https://www.ncbi.nlm.nih.gov/pubmed/?term=4759581 Longitudinal Studies Design .   No .
Study Results reading medical literature;diagnostic test;study results Journal Article Users' Guides to the Medical Literature III. How to use an article about a diagnostic test A. Are the Results of the study valid? https://www.ncbi.nlm.nih.gov/pubmed/?term=8283589 Reading Medical Literature .   No .
Summaries Statistics; analysis; summaries Online Interactive Course Exploratory Data Analysis https://www.coursera.org/learn/exploratory-data-analysis Per the course website, this is course 4 of 10 in the Data Science Specialization. It is prepared by JHU and helps cover the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. . Any Yes .
Surgical Methods propensity scores;surgical methods Other using Propensity Scores to Adjust for Group Differences: Examples comparing Alternative Surgical Methods http://www2.sas.com/proceedings/sugi25/25/st/25p261.pdf Propensity Scores .   No .
Survey survey Online Interactive Course Survey Data Collection and Analytics Specialization https://www.coursera.org/specializations/data-collection Per the course website, this 7-course specialiation prepard by the Univerisuty of Michigan covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you . Any Yes .
Survival Analysis survival analysis Book Survival analysis using SAS: A practical Guide https://www.amazon.com/dp/1599946408 Easy to read and comprehensive is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. 3 SAS Yes 2
Survival Analysis Exact confidence intervals for a proportion; bootstrap; competing risk; case-control matching; survival analysis Software to Download Locally Written SAS Macros http://www.mayo.edu/research/departments-divisions/department-health-sciences-research/division-biomedical-statistics-informatics/software/locally-written-sas-macros The SAS macros below were written and are maintained by Mayo Clinic staff. They contain SAS source code, a brief description of the macro's function and an example of the macro call. 4 SAS No 3
Survival Analysis survival analysis; Kaplan-Meier; log rank; Cox regression; Cox model; proportional hazard model Journal Article Statistics review 12: Survival analysis https://www.ncbi.nlm.nih.gov/pubmed/15469602 This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan-Meier methods, log rank test and Cox's proportional hazards model are described. 3   No .
Survival Analysis survival analysis; censoring; truncation; Kaplan-Meier; Logrank test; Cox model; life table Book Survival Analysis: Techniques for Censored and Truncated Data:2nd (Second) edition https://www.amazon.com/Survival-Analysis-Techniques-Censored-Truncated/dp/B0086HX5FQ survival analysis concepts and strategies 4 Any No 4
Survival Analysis survival analysis;regression; Journal Article PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression. https://www.ncbi.nlm.nih.gov/pubmed/?term=25572709 We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sam