The False-positive to False-negative Ratio in Epidemiologic Studies
Abstract
The ratio of false-positive to false-negative findings (FP:FN ratio) is an informative metric that warrants further evaluation. The FP:FN ratio varies greatly across different epidemiologic areas. In genetic epidemiology, it has varied from very high values (possibly even >100:1) for associations reported in candidate-gene studies to very low values (1:100 or lower) for associations with genome-wide significance. The substantial reduction over time in the FP:FN ratio in human genome epidemiology has corresponded to the routine adoption of stringent inferential criteria and comprehensive, agnostic reporting of all analyses. Most traditional fields of epidemiologic research more closely follow the practices of past candidate gene epidemiology, and thus have high FP:FN ratios. Further, FP and FN results do not necessarily entail the same consequences, and their relative importance may vary in different settings. This ultimately has implications for what is the acceptable FP:FN ratio and for how the results of published epidemiologic studies should be presented and interpreted.
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The lower the power of a study, the lower the probability that an observed effect passing nominal statistical significance actually reflects a true effect.
“Second, the lower the power of a study, the lower the probability that an observed effect that passes the required threshold of claiming its discovery (that is, reaching nominal statistical significance, such as p < 0.05) actually reflects a true effect 1,12 .”
From Power failure: why small sample size undermines the reliability of neuroscience (2013) · cites this paper as [12]
Previously almost all of the proposed candidate gene associations from small studies were false, with some exceptions, but collaborative consortia have substantially improved power, and the replicated results can be considered highly reliable.
“Although previously almost all of the proposed candidate gene associations from small studies were false 99 (with some exceptions 100 ), collaborative consortia have substantially improved power, and the replicated results can be considered highly reliable.”
From Power failure: why small sample size undermines the reliability of neuroscience (2013) · cites this paper as [99]
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- Authors
- Ioannidis, J. P. A., Tarone, R., & McLaughlin, J. K.
- Affiliations
- Stanford Medicine (US)
- International Epidemiology Institute (US)
- Corresponding author
- Ioannidis, John P. A.
- Venue
- Epidemiology
- Published
- 2011-07
- Volume
- 22
- Issue
- 4
- Pages
- 450-456
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2005317772
- Cited by
- 321
- References
- 66
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 16 · 2023: 19 · 2022: 28 · 2021: 18 · 2020: 24 · 2019: 21 · 2018: 22 · 2017: 30 · 2016: 26 · 2015: 19 · 2014: 21 · 2013: 21 · 2012: 16
- FWCI
- 21.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- false positives, false-negative results, genetic epidemiology, candidate gene association, reporting practices, statistical significance thresholds
- MeSH terms
- Data Interpretation, Statistical, False Negative Reactions, False Positive Reactions, Humans, Epidemiologic Research Design, Molecular Epidemiology, Genome-Wide Association Study
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