Multiple Comparisons Using R
About this ebook
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.
About the author
Frank Bretz is Global Head of the Statistical Methodology group at Novartis Pharma AG in Basel, Switzerland. He is also an adjunct professor at the Hannover Medical School in Germany.
Torsten Hothorn is a professor of biostatistics in the Faculty of Mathematics, Computer Science and Statistics at Ludwig-Maximilians-Universität München in Germany.
Peter Westfall is James and Marguerite Niver and Paul Whitfield Horn Professor of Statistics and associate director of the Center for Advanced Analytics and Business Intelligence at Texas Tech University in Lubbock, USA.
