CALCULATING AVERAGE POWER FOR THE BENJAMINI-HOCHBERG PROCEDURE

William J Feser; Tasha E Fingerlin; Matthew J Strand; Deborah H Glueck

CALCULATING AVERAGE POWER FOR THE BENJAMINI-HOCHBERG PROCEDURE

J Stat Theory Appl. 2009;8(3):325-352.

Authors

William J Feser¹, Tasha E Fingerlin², Matthew J Strand³, Deborah H Glueck²

Affiliations

¹ Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver; A portion of the work reported here was completed in partial fulfillment of the requirements for the Masters degree in Biostatistics from the University of Colorado Denver.
² Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver.
³ Division of Biostatistics, National Jewish Health.

PMID: 27818616
PMCID: PMC5095931

Abstract

Using exact, analytic results for the average power of the Benjamini-Hochberg (1995) procedure, we provide example power analyses useful for scientists planning studies involving multiple comparisons. The power results are based on the distribution of the p-value under the alternative for the Pearson's χ², and for the Hotelling-Lawley trace, the Wilks' lambda, and the Pillai-Bartlett trace, all tests for the general linear multivariate model. Detailed example power analyses are given for a planned mammography experiment with categorical data and a study that tests the association of a single nucleotide polymorphism with insulin resistance and visceral adiposity.

Keywords: Power; multiple comparisons.

Abstract

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