JSM 2004 - Toronto

Abstract #300118

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Activity Number: 363
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300118
Title: Parametric and Nonparametric Empirical Bayes Adjustments to Multiple P Values for the Detection of Differentially Expressed Genes in Microarray Experiments
Author(s): Susmita Datta*+
Companies: Georgia State University
Address: Dept. of Mathematics and Statistics, Atlanta, GA, 30303-3083,
Keywords: p values ; microarray ; empirical Bayes ; differentially expressed genes
Abstract:

An empirical Bayes adjustment to multiple t-tests has been shown to improve the sensitivity of the overall procedure. Here we propose an empirical Bayes adjustment to the p values rather than the test statistics. As a result, it is applicable to other types of multiple tests (e.g., F, chi-squared) as well. Both parametric and nonparametric versions of the empirical Bayes adjustment are considered. Thus, each p value, in turn, borrows evidence from other p values across the tests. A new set of accept/reject decisions are reached for each null hypothesis using the empirical Bayes adjusted p values through a resampling based step-down p value calculation that protects the analyst against the overall (familywise) Type 1 error rate. The new procedure is shown to produce further improvement in sensitivity in a number of examples.

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