Multiple Comparisons Using R

Regular price €107.99
A01=Frank Bretz
A01=Peter Westfall
A01=Torsten Hothorn
adaptive designs
adjustment
advanced data analysis
all pairwise comparisons
Author_Frank Bretz
Author_Peter Westfall
Author_Torsten Hothorn
Bonferroni
Bonferroni Test
Category=PBT
Category=PS
clinical trial statistics
Closed Test Procedures
Closure Principle
confidence
Dunnett Test
Elementary Hypotheses
Elementary Null Hypotheses
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
experimental design
Familywise Error Rate
General Linear Hypotheses
general linear models
Group Sequential Design
group sequential designs
High Throughput Screenings
Hochberg Procedure
Holm Procedure
hypothesis
hypothesis testing
Interim Analysis
Intersection Hypotheses
intervals
mixed-effects models
multcomp
Multcomp Package
Multiple Comparison Procedures
multiple comparisons
multiple hypotheses testing
multiplicity
Multiplicity Adjustment
multiplicity problems
Null Hypotheses
Null Hypothesis H1
package
parametric model comparison techniques
parametric models
Ppp Ppp Ppp Ppp
procedure
R
regression analysis
resampling
Simes
simultaneous
Simultaneous Confidence Band
Simultaneous Confidence Intervals
statistical inference
Stepwise Test Procedures
survival data
Tukey Contrasts
Union Intersection Test

Product details

  • ISBN 9781584885740
  • Weight: 460g
  • Dimensions: 156 x 234mm
  • Publication Date: 27 Jul 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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.