ArticleBiometrika2015
Optimal multiple testing under a Gaussian prior on the effect sizes.
Article in Biometrika, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Covariate adaptive familywise error rate control for genome-wide association studies.Biometrika · 2021Article
- Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes.Biostatistics (Oxford, England) · 2021Article
- Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing.Nature communications · 2019Article
- Weighted mining of massive collections of [Formula: see text]-values by convex optimization.Information and inference : a journal of the IMA · 2018Article
- Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.Nature methods · 2016Article
- Genome-Wide Scan Informed by Age-Related Disease Identifies Loci for Exceptional Human Longevity.PLoS genetics · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal [Formula: see text]-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.
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Registered trials
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