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Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

ABSTRACT: The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.

SUBMITTER: Ni Y

PROVIDER: S-EPMC10970028 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

Ni Yonghui Y Seffernick Anna Eames AE Onar-Thomas Arzu A Pounds Stanley B SB

Genes 20240307 3

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i>p</i>-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i>FDRsample ...[more]

PMID: 38540403

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