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Modified FDR Controlling Procedure for Multi-Stage Analyses

Author

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  • Tuglus Catherine

    (University of California, Berkeley)

  • van der Laan Mark J.

    (University of California, Berkeley)

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.

Suggested Citation

  • Tuglus Catherine & van der Laan Mark J., 2009. "Modified FDR Controlling Procedure for Multi-Stage Analyses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-17, February.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:12
    DOI: 10.2202/1544-6115.1397
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    References listed on IDEAS

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    1. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    2. van der Laan Mark J., 2006. "Statistical Inference for Variable Importance," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-33, February.
    3. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    Cited by:

    1. Goldstein Benjamin A & Polley Eric C & Briggs Farren B. S., 2011. "Random Forests for Genetic Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, July.

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