BERD Resource by Keyword

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)
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
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
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
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
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 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
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
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
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
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
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 .
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
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 .
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
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 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 .
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 .
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
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
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
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
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 .
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
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
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 .
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
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 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
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. 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
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
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 .
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
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 .
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 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 .
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
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
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 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
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 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
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
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 .
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 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 .
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 .
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 .
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 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
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 .
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 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
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 .
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 .
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
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
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
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
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 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
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
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 .
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
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
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 .
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 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
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
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 .
T-Test t-test Journal Article Statistics review 5: Comparison of means https://www.ncbi.nlm.nih.gov/pubmed/12398782 The present review introduces the commonly used t-test, used to compare a single mean with a hypothesized value, two means arising from paired data, or two means arising from unpaired data. The assumptions underlying these tests are also discussed. 2   No 2
T-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
Tables 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 .
Textbook 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
Type I Error 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
Wilcoxon Rank Sum 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 .
Wilcoxon Signed Rank 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 .