The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A Direct Approach to False Discovery Rates
Abstract
Summary: Multiple-hypothesis testing involves guarding against much more complicated errors than single-hypothesis testing. Whereas we typically control the type I error rate for a single-hypothesis test, a compound error rate is controlled for multiple-hypothesis tests. For example, controlling the false discovery rate FDR traditionally involves intricate sequential p-value rejection methods based on the observed data. Whereas a sequential p-value method fixes the error rate and estimates its corresponding rejection region, we propose the opposite approach—we fix the rejection region and then estimate its corresponding error rate. This new approach offers increased applicability, accuracy and power. We apply the methodology to both the positive false discovery rate pFDR and FDR, and provide evidence for its benefits. It is shown that pFDR is probably the quantity of interest over FDR. Also discussed is the calculation of the q-value, the pFDR analogue of the p-value, which eliminates the need to set the error rate beforehand as is traditionally done. Some simple numerical examples are presented that show that this new approach can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.
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“The 2982 MLG that contained over 100 genes were tested for enrichment or depletion in individuals with ACVD (218 ACVD cases and 187 controls) according to the non-parametric method, Wilcoxon rank-sum test, and controlled for multiple testing (q-value <0.05)55, 60.”
From The gut microbiome in atherosclerotic cardiovascular disease (2017) · cites this paper as [60]
Citations
Cites
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Self-citationOn the adaptive control of the false discovery rate in multiple testing with independent statistics
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Research works printed but not linked: 1
- Storey, 2001, Estimating false discovery rates under dependence, with applications to DNA microarrays
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Metadata
- Authors
- Storey, J. D.
- Affiliations
- Stanford University (US)
- Corresponding author
- Storey, John D.
- ORCID
- Storey, John D.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-08-01
- Volume
- 64
- Issue
- 3
- Pages
- 479-498
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2105381419
- Cited by
- 5,603
- References
- 12
- Citations by year
- 2026: 164 · 2025: 183 · 2024: 239 · 2023: 245 · 2022: 201 · 2021: 278 · 2020: 249 · 2019: 249 · 2018: 279 · 2017: 274 · 2016: 297 · 2015: 255 · 2014: 340 · 2013: 348 · 2012: 363
- FWCI
- 96.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- false discovery rate, multiple comparisons, Q-value, Benjamini-Hochberg procedure, statistical power
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