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Power and sample size estimation in high dimensional biology

Department of Mathematics and Statistics, University of Missouri - Rolla, MO, USA

Grier P Page

USDA ARS, Department of Agronomy, Iowa State University, Ames, IA, USA

Jode Edwards

USDA ARS, Department of Agronomy, Iowa State University, Ames, IA, USA

Tsuyoshi Kayo

Wisconsin Regional Primate Research Center, Madison, WI, USA

Tomas A Prolla

Department of Genetics and Medical Genetics, University of Wisconsin, Madison, WI, USA

Richard Weindruch

Department of Medicine, University of Wisconsin and The Geriatric Research, Education, and Clinical Center, William S Middleton VA Hospital, Madison, WI, USA

Paska A Permana

Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA

John D Mountz

The Birmingham Veterans Administration Medical Center, University of Alabama at Birmingham, Birmingham, AL, USA

David B Allison

Department of Biostatistics, Section on Statistical Genetics, and Clinical Nutrition Research Center, University of Alabama at Birmingham, Birmingham, AL, USA, dallison{at}ms.soph.uab.edu

Genomic scientists often test thousands of hypotheses in a single experiment. One example is a microarray experiment that seeks to determine differential gene expression among experimental groups. Planning such experiments involves a determination of sample size that will allow meaningful interpretations. Traditional power analysis methods may not be well suited to this task when thousands of hypotheses are tested in a discovery oriented basic research. We introduce the concept of expected discovery rate (EDR) and an approach that combines parametric mixture modelling with parametric bootstrapping to estimate the sample size needed for a desired accuracy of results. While the examples included are derived from microarray studies, the methods, herein, are ‘extraparadigmatic’ in the approach to study design and are applicable to most high dimensional biological situations. Pilot data from three different microarray experiments are used to extrapolate EDR as well as the related false discovery rate at different sample sizes and thresholds.

Statistical Methods in Medical Research, Vol. 13, No. 4, 325-338 (2004)
DOI: 10.1191/0962280204sm369ra


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