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Abstract Details
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Activity Number:
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316
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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General Methodology
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| Abstract - #300246 |
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Title:
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Permutation Multiple Tests of Binary Features May Not Control Error Rates
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Author(s):
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Eloise Kaizar*+ and Yan Li and Jason C. Hsu
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Companies:
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The Ohio State University and Amylin Pharmaceuticals Inc. and The Ohio State University
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Address:
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, , OH, 43210,
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Keywords:
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Multiple tests ;
Permutation ;
FWER ;
pharmacogenomics
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Abstract:
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Multiple testing for significant association between predictors and responses has a wide array of applications. One such application is pharmacogenomics, where testing for association between responses and many genetic markers is of interest. Permuting response group labels to generate a reference distribution is often thought of as a convenient thresholding technique that automatically captures dependence in the data. In reality, non-trivial model assumptions are required for permutation testing to control multiple testing error rates. When binary predictors (such as genetic markers) are individually tested by standard tests, permutation multiple testing can give incorrect unconditional and, especially, conditional assessment of significances, and thus misleading results.
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