Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment
Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.
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Binary Data Applications
Munchausens Statistical Grid
The MULTTEST Procedure
Using Software for the Analysis
List of Algorithms
adjusted p-values Algorithm analyzed applications approximately assumed asymptotically binary data binomial bootstrap adjustments bootstrap data bootstrap resampling carcinogenicity Chapter Cochran-Armitage coefficients complete null hypothesis computed confidence intervals considered counting variables covariance data sets denote dependence structure dose effects estimate example experiment experimental Fisher exact free step-down heteroscedastic homoscedastic homoscedastic normal incorporating independent lower-tailed matrix mean multiple comparisons multiple testing multiplicity adjustment multiplicity-adjusted multivariate normal NBOOT Needleman nonnormal normal distribution oat bran observed pairwise comparisons parameters particular performed permutation distribution pooled probability PROC MULTTEST procedure r-statistic random variables raw p-values Raw.p Adj-p regression rejected replacement sample rerandomization resampled data sets resampling method resampling-based adjustments residuals response variables sample sizes scores Section single-step adjusted standard statistically significant step-down adjustments subset pivotality test statistics Thiouracil Thyroxin treatment groups trend true tumor Type I errors unadjusted univariate upper-tailed values variance weight Westfall Z-test zero
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