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Related Experiment Videos

Sample size determination for the false discovery rate.

Stan Pounds1, Cheng Cheng

  • 1Department of Biostatistics, St Jude Children's Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA. stanley.pounds@stjude.org

Bioinformatics (Oxford, England)
|October 6, 2005
PubMed
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A new method, anticipated false discovery rate (aFDR), helps determine sample size for experiments using statistical significance. This approach provides a general algorithm for sample size determination, especially for k-group comparisons.

Area of Science:

  • Biostatistics
  • Statistical Significance
  • Experimental Design

Background:

  • A widely applicable method for determining sample size based on false discovery rate (FDR) is lacking.
  • Accurate sample size determination is crucial for robust experimental design and statistical validity.

Purpose of the Study:

  • To propose and develop the anticipated false discovery rate (aFDR) as a novel tool for sample size determination.
  • To provide a general algorithm for sample size calculation based on aFDR and anticipated statistical power.

Main Methods:

  • Derived mathematical expressions for aFDR and anticipated average statistical power.
  • Developed a general algorithm for sample size determination.
  • Implemented and validated the algorithm for k-group comparisons (k >= 2).

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Main Results:

  • The proposed aFDR method provides a conceptual tool for sample size determination.
  • The general algorithm effectively determines sample size for k-group comparisons.
  • The algorithm demonstrated strong performance in traditional and real-data simulations.

Conclusions:

  • The aFDR offers a practical solution for sample size calculation in experiments relying on FDR.
  • The developed algorithm is applicable to various experimental designs, particularly those involving multiple group comparisons.
  • Freely available S-plus and R code facilitate the implementation of this method.