Sample size determination for the false discovery rate.

Stan Pounds; Cheng Cheng

doi:10.1093/bioinformatics/bti699

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Sample size determination for the false discovery rate.

Stan Pounds·Cheng Cheng

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Abstract

MOTIVATION: There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR). RESULTS: We propose and develop the anticipated FDR (aFDR) as a conceptual tool for determining sample size. We derive mathematical expressions for the aFDR and anticipated average statistical power. These expressions are used to develop a general algorithm to determine sample size. We provide specific details on how to implement the algorithm for a k-group (k > or = 2) comparisons. The algorithm performs well for k-group comparisons in a series of traditional simulations and in a real-data simulation conducted by resampling from a large, publicly available dataset. AVAILABILITY: Documented S-plus and R code libraries are freely available from www.stjuderesearch.org/depts/biostats.

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Algorithms Computational Biology Computer Simulation Data Interpretation, Statistical Databases, Protein False Positive Reactions Gene Expression Profiling Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Reproducibility of Results Sample Size Software Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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Stan Pounds, Cheng Cheng. 2005-10-04. Sample size determination for the false discovery rate.. https://doi.org/10.1093/bioinformatics%2Fbti699

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