Sample size determination for the false discovery rate

Bioinformatics. 2005 Dec 1;21(23):4263-71. doi: 10.1093/bioinformatics/bti699. Epub 2005 Oct 4.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • 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