Multiple Comparisons in SAS®: Classical to Modern Methods
Startdaten und Startorte
Beschreibung
Students evaluate the rationale for multiple comparisons and multiple testing procedures (MCPs), the different types of MCPs, and when to use them. Students learn how to analyze data using SAS software and how to use state-of-the-art MCPs that enable nonnormal distributions, incorporate correlation structures and logical dependencies, and generally include the most powerful and flexible methods currently available. ..The primary applications are taken from pharmaceutical science and biotechnology, including genomics, and additional applications are taken from business intelligence, agriculture, social science, epidemiology, and engineering. Statistical analyses include MCPs in studies with …
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Students evaluate the rationale for multiple comparisons and multiple testing procedures (MCPs), the different types of MCPs, and when to use them. Students learn how to analyze data using SAS software and how to use state-of-the-art MCPs that enable nonnormal distributions, incorporate correlation structures and logical dependencies, and generally include the most powerful and flexible methods currently available. ..The primary applications are taken from pharmaceutical science and biotechnology, including genomics, and additional applications are taken from business intelligence, agriculture, social science, epidemiology, and engineering. Statistical analyses include MCPs in studies with multiple outcome measures, multiple group comparisons, regression response surface comparisons, and functional data. Recent releases of SAS contain the most powerful MCPs currently available, enabling researchers to control both Type I and Type II errors at acceptable levels. In addition, new ODS graphics displays make the results of multiple comparisons easy to understand and present. ..The course uses primarily the GLIMMIX and MULTTEST procedures in SAS/STAT, which subsume the MCP functionalities of the GLM and MIXED procedures, and provide additional powerful methods and graphical output. Students explore these procedures using a series of hands-on exercises. Upon completion of the course, participants will be able to use and display results from the best and most modern multiple comparisons in a wide variety of practical settings..
Voraussetzungen
Before attending this course, you should be familiar with the basic structure and concepts of SAS (DATA step and SAS/STAT procedures such as PROC REG and PROC GLM). You should be familiar with the core concepts in descriptive statistics, probability, p-values and confidence intervals, Type I and Type II errors, multiple regression and correlation, and analysis of variance. Familiarity with multivariate analysis, mixed linear models, nonparametrics, analysis of binary data, and generalized linear models are helpful, but not assumed.
Zielgruppe
Statisticians, scientists, and managers who work for pharmaceutical, bioscience, and biotechnology companies or government agencies; academic researchers in psychology, biology, education, social science, medicine, engineering, and business; editorial staff of scientific and technical journals; statistical support personnel who need to assist researchers in selecting appropriate statistical tools
Module
tbd
Kursinhalte
- Multiple Comparisons and Multiple Tests Using SAS
- review of hypothesis testing and confidence intervals
- analysis of data from a study of a cold remedy
- sources of multiplicity and effects of multiplicity
- multiple comparisons and multiple tests: basic notions
- Classical to Modern Multiple Comparisons Methods for Control of
FWER in Normal Linear Models
- Dunnett's method
- Tukey's method
- simulation-based methods
- Power and Sample Size Determination for Multiple Comparisons
Procedures
- power calculation and sample size determination using individual power
- more general power calculation
- Step-Down Testing Methods
- Holm's step-down testing method
- free step-down testing method using correlations
- logically constrained step-down method: Shaffer's Bonferroni-based method
- step-down method using logical constraints and correlations: the extended Shaffer-Royen method
- Step-Down Testing with Non-Normal, Heteroscedastic, and
Correlated Data
- PROC GLIMMIX solution for heteroscedasticity
- multiple comparisons with repeated measures data
- multiple comparisons with alternative data distributions
- Nonparametric Resampling-Based Testing Using the MULTTEST
Procedure
- the MULTTEST procedure
- bootstrap multiple testing
- permutation multiple testing
- Closed Testing Procedures: Hommel, Hochberg, Gatekeepers, and
Fixed Sequences
- the Simes test and Hommel's method
- Hochberg's method
- closed testing using the Fisher combination test
- fixed sequence and gatekeeping procedures
- Controlling the False Discovery Rate
- the Benjamini-Hochberg false discovery rate controlling method
- using PROC MULTTEST to adjust for multiplicity using the Benjamini-Hochberg method
- Recommendations and Summary
- summary
- final words: Bonferroni
- key references: books
- key references: journal articles
Referent
This course will be presented by Peter Westfall, Ph.D., professor of statistics at Texas Tech University.
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