JSM 2005 - Toronto

Abstract #302312

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 76
Type: Invited
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302312
Title: Generally Valid Resampling-based Multiple Testing Methods
Author(s): Katherine S. Pollard*+ and Sandrine Dudoit and Mark van der Laan
Companies: University of California, Santa Cruz and University of California, Berkeley and University of California, Berkeley
Address: 1156 High Street, Santa Cruz, CA, 95064,
Keywords: Multiple hypothesis testing ; multivariate ; bootstrap ; genomics
Abstract:

We have developed generally valid resampling-based single-step and stepwise multiple testing procedures (MTP) for control of a broad class of Type I error rates, including tail probabilities and expected values of arbitrary functions of the numbers of Type I errors and rejected hypotheses. A key feature of the methodology is the test statistics null distribution (rather than data null distribution), which does not require the subset pivotality condition, and therefore allows one to test hypotheses about a much broader class of parameters (e.g., correlations, regression parameters in survival models) than are covered by currently available methods. Therefore, our methods are widely applicable to testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics. I have implemented these MTPs in the "multitest" package of the open source bioconductor project (www.bioconductor.org), which provides a simple bootstrap estimator of the joint null distribution of the vector of test statistics.

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Revised March 2005