2009/11/01 by Kathryn Roeder, Larry Wasserman · 108 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Causal Inference Techniques #Control (management) #False discovery rate #Genetic Associations and Epidemiology #Multiple comparisons problem #Statistical Methods in Clinical Trials #Statistical hypothesis testing #Statistical power #Type I and type II errors #stat.ME
paper · pdf · doi:10.1214/09-sts289
published in Statistical Science 24(4), 398-413 (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/09-STS289 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2009/11/01 · arxiv created 2010/10/22 · arxiv updated 2010/10/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered using weighted p-values, with the motivation often based upon the scientific plausibility of the hypotheses. We review this literature, derive optimal weights and show that the power is remarkably robust to misspecification of these weights. We consider two methods for choosing weights in practice. The first, external weighting, is based on prior information. The second, estimated weighting, uses the data to choose weights.