JSM 2005 - Toronto

Abstract #302990

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Activity Number: 188
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #302990
Title: Sample Size Calculation for Multiple Testing in Microarray Data Analysis
Author(s): Heejung Bang*+ and Sin-Ho Jung and S. Stanley Young
Companies: Cornell University and Duke University and National Institute of Statistical Sciences
Address: 411 East 69th St, New York, NY, 10021, United States
Keywords: Adjusted p-value ; Bonferroni ; Multi-step ; Permutation ; Simulation ; Single-step
Abstract:

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are used frequently for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni-adjustment has been used widely to circumvent this problem. It is well known; however, the standard Bonferroni test often is found conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method, and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the familywise error rate accurately controlled at the desired level. We also present a sample-size calculation method for designing microarray studies.

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Revised March 2005