Multiple Comparisons Using R

Frank Bretz, Torsten Hothorn, Peter Westfall

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July 27, 2010 by Chapman and Hall/CRC
Professional - 205 Pages - 44 B/W Illustrations
ISBN 9781584885740 - CAT# C5742

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Features

    • Covers a range of multiple comparison procedures, from the Bonferroni method and Simes’ test to resampling and adaptive design methods
    • Discusses how to prevent finding artifacts through unreasonable or excessive data manipulation
    • Examines the ability to state rigorous claims in spite of the hazards of multiplicity
    • Provides the theoretical framework for various applications
    • Focuses on classical applications of multiple comparison procedures
    • Explores how to determine what is a safe and effective dose as well as whether a new condition or treatment is successful
    • Implements many examples using R
    • Offers R packages and source code at http://CRAN.R-project.org

    Summary

    Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org

    After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques.

    Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa.

    See Dr. Bretz discuss the book.