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CALCULATING AVERAGE POWER FOR THE BENJAMINI-HOCHBERG PROCEDURE.
William J Feser1, Tasha E Fingerlin2, Matthew J Strand3
1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver; A portion of the work reported here was completed in partial fulfillment of the requirements for the Masters degree in Biostatistics from the University of Colorado Denver.
Journal of Statistical Theory and Applications : JSTA
|November 8, 2016
Summary
This study provides power analyses for the Benjamini-Hochberg procedure, aiding scientists in planning studies with multiple comparisons. These results, based on p-value distributions, are applicable to various statistical tests and real-world experiments.
Area of Science:
- Statistics
- Biostatistics
- Genetics
Background:
- Multiple comparisons in scientific studies can inflate Type I error rates.
- Accurate power analysis is crucial for designing studies with sufficient statistical power to detect true effects.
Purpose of the Study:
- To provide exact, analytic results for the average power of the Benjamini-Hochberg procedure.
- To offer practical power analyses for scientists planning studies involving multiple comparisons.
Main Methods:
- Utilized exact, analytic results for the average power of the Benjamini-Hochberg procedure.
- Based power calculations on the distribution of the p-value under the alternative hypothesis.
- Applied methods to Pearson's chi-squared, Hotelling-Lawley trace, Wilks' lambda, and Pillai-Bartlett trace tests for the general linear multivariate model.
Main Results:
- Provided example power analyses for the Benjamini-Hochberg procedure.
- Demonstrated the application of these power analyses to specific scientific scenarios.
Conclusions:
- The provided power analyses are valuable tools for scientists designing studies with multiple comparisons.
- The methods are applicable to diverse statistical tests and experimental designs, including those in medical research.
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