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Bioinformatics Advance Access published online on August 9, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti604
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received April 26, 2005
Revised July 1, 2005
Accepted July 28, 2005

Article

Two-stage designs for experiments with a large number of hypotheses

Sonja Zehetmayer 1, Peter Bauer 1, and Martin Posch 1*

1 Section of Medical Statistics, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria

* To whom correspondence should be addressed.
Martin Posch, E-mail: Martin.Posch{at}meduniwien.ac.at


  Abstract

Motivation: When a large number of hypotheses are investigated the false discovery rate (FDR) is commonly applied in gene expression analysis or gene association studies. Conventional single-stage designs may lack of power due to low sample sizes for the individual hypotheses. We propose two-stage designs where the first stage is used to screen the "promising" hypotheses which are further investigated at the second stage with an increased sample size. A multiple test procedure based on sequential individual p-values is proposed to control the FDR for the case of independent normal distributions with known variance.

Results: The power of optimal two-stage designs is impressively larger than the power of the corresponding single-stage design with equal costs. Extensions to the case of unknown variances, and correlated test statistics are investigated by simulations. Moreover, it is shown that the simple multiple test procedure using first stage data for screening purposes and deriving the test decisions only from second stage data is a very powerful option.

Availability: An R-program is available on http://www.meduniwien.ac.at/medstat/research/fdr/application.R.


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