Modified FDR Controlling Procedure for Multi-Stage Analyses

Tuglus Catherine and J. van der Laan Mark
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Tuglus Catherine: University of California, Berkeley
J. van der Laan Mark: University of California, Berkeley

Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 17

Abstract: Multiple testing has become an integral component in genomic analyses involving microarray experiments where a large number of hypotheses are tested simultaneously. However, before applying more computationally intensive methods, it is often desirable to complete an initial truncation of the variable set using a simpler and faster supervised method such as univariate regression. Once such a truncation is completed, multiple testing methods applied to any subsequent analysis no longer control the appropriate Type I error rates. Here we propose a modified marginal Benjamini & Hochberg step-up FDR controlling procedure for multi-stage analyses (FDR-MSA), which correctly controls Type I error in terms of the entire variable set when only a subset of the initial set of variables is tested. The method is presented with respect to a variable importance application. As the initial subset size increases, we observe convergence to the standard Benjamini & Hochberg step-up FDR controlling multiple testing procedures. We demonstrate the power and Type I error control through simulation and application to the Golub Leukemia data from 1999.

Keywords: false discovery rate; modified FDR; targeted maximum likelihood (search for similar items in EconPapers)
Date: 2009
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DOI: 10.2202/1544-6115.1397

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