The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
UK Biobank, big data, and the consequences of non-representativeness
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“Importantly, selection bias can result in AI models trained on a sample that differs considerably from the general population 161 , thus hurting these models at inference time 162”
From Multimodal biomedical AI (2022) · cites this paper as [162]
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Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979
Transportability of Trial Results Using Inverse Odds of Sampling Weights
Westreich, D. J., Edwards, J. K., Lesko, C. R., Stuart, E. A., & Cole, S. R. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx164 · Cited by 324
Self-citationGeneralizing Study Results: A Potential Outcomes Perspective
Lesko, C. R., Buchanan, A. L., Westreich, D., Edwards, J. K., Hudgens, M. G., & Cole, S. R. (2017) · Epidemiology · doi:10.1097/ede.0000000000000664 · Cited by 269
Self-citationTarget Validity and the Hierarchy of Study Designs
Westreich, D., Edwards, J. K., Lesko, C. R., Cole, S. R., & Stuart, E. A. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwy228 · Cited by 165
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Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
“Importantly, selection bias can result in AI models trained on a sample that differs considerably from the general population 161 , thus hurting these models at inference time 162 .”
BackgroundBatty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J., & Bell, S. (2020) · BMJ · doi:10.1136/bmj.m131 · Cited by 635
Gao, X., Geng, T., Jiang, M., Huang, N., Zheng, Y., Belsky, D. W., & Huang, T. (2023) · Nature Communications · doi:10.1038/s41467-023-38013-7 · Cited by 306
Genetic analyses identify widespread sex-differential participation bias
Pirastu, N., Cordioli, M., Nandakumar, P., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., Rajagopal, V. M., Parolo, P. D. B., Baya, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00846-7 · Cited by 251
Initiative, N. H. U. B., Julkunen, H., Cichońska, A., Slagboom, P. E., & Würtz, P. (2021) · eLife · doi:10.7554/elife.63033 · Cited by 200
“Even though this is generally not a concern for investigating risk associations (Keyes and Westreich, 2019), it does limit the statistical power to explore effects of ethnicity and old age.”
Background
Metadata
- Authors
- Keyes, K. M., & Westreich, D.
- Affiliations
- Universidad Mayor (CL)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Keyes, Katherine M
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 393
- Issue
- 10178
- Pages
- 1297
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7825643
- OpenAlex
- W2928514898
- Cited by
- 187
- References
- 4
- Citations by year
- 2026: 21 · 2025: 26 · 2024: 36 · 2023: 26 · 2022: 32 · 2021: 20 · 2020: 19 · 2019: 7
- FWCI
- 15.41
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UNC Libraries
- Funders
- National Institute on Alcohol Abuse and Alcoholism (K01AA021511)
- National Instititues of Health (OD/NICHD DP2-HD-08–4070)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- UK Biobank, sample representativeness, big data, causal effect estimation, generalizability of study results
- MeSH terms
- Big Data, Environmental Exposure, United Kingdom, Health Resources, Humans, Volunteers, Biological Specimen Banks, Health Information Systems
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