Collider bias undermines our understanding of COVID-19 disease risk and severity
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Abstract
Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.
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Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
Self-citationAcosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
Menni, C., Klaser, K., May, A., Polidori, L., Capdevila, J., Louca, P., Sudre, C. H., Nguyen, L. H., Drew, D. A., Merino, J., et al. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00224-3 · Cited by 1,031 · see citing works
Graham, M. S., Sudre, C. H., May, A., Antonelli, M., Murray, B., Varsavsky, T., Kläser, K., Canas, L. S., Molteni, E., Modat, M., et al. (2021) · The Lancet Public Health · doi:10.1016/s2468-2667(21)00055-4 · Cited by 304 · see citing works
Richardson, T. G., Leyden, G. M., Wang, Q., Bell, J. A., Elsworth, B., Davey Smith, G., & Holmes, M. V. (2022) · PLOS Biology · doi:10.1371/journal.pbio.3001547 · Cited by 304 · see citing works
Self-citationHolmberg, M. J., & Andersen, L. W. (2022) · JAMA · doi:10.1001/jama.2022.1820 · Cited by 229 · see citing works
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
Adams, K., Rhoads, J. P., Surie, D., Gaglani, M., Ginde, A. A., McNeal, T., Talbot, H. K., Casey, J. D., Zepeski, A., Shapiro, N. I., et al. (2022) · BMJ · doi:10.1136/bmj-2022-072065 · Cited by 115 · see citing works
Brayne, C., & Moffitt, T. E. (2022) · Nature Aging · doi:10.1038/s43587-022-00277-x · Cited by 89 · see citing works
Uncovering survivorship bias in longitudinal mental health surveys during the COVID-19 pandemic
Czeisler, M. É., Wiley, J. F., Czeisler, C. A., Rajaratnam, S. M. W., & Howard, M. E. (2021) · Epidemiology and Psychiatric Sciences · doi:10.1017/s204579602100038x · Cited by 90 · see citing works
High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data
Zang, C., Zhang, H., Xu, J., Zhang, H., Fouladvand, S., Havaldar, S., Cheng, F., Chen, K., Chen, Y., Glicksberg, B. S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43929-1 · Cited by 69 · see citing works
Cross-sectional studies: strengths, limitations, and methodological considerations
Baumeister, S., Kocher, T., Papapanou, P. N., Holtfreter, B., & Demmer, R. T. (2026) · Journal of Periodontal Research · doi:10.1111/jre.70063 · Cited by 38 · see citing works
ChatGPT identifies gender disparities in scientific peer review
Verharen, J. P. (2023) · eLife · doi:10.7554/elife.90230 · Cited by 16 · see citing works
Transparency of COVID-19-Related Research in Dental Journals
Sofi-Mahmudi, A., & Raittio, E. (2022) · Frontiers in Oral Health · doi:10.3389/froh.2022.871033 · Cited by 15 · see citing works
- Authors
- Griffith, G. J., Morris, T. T., Tudball, M. J., Herbert, A., Mancano, G., Pike, L., Sharp, G. C., Sterne, J., Palmer, T. M., Davey Smith, G., Tilling, K., Zuccolo, L., Davies, N. M., & Hemani, G.
- Affiliations
- University of Bristol (GB)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Hemani, Gibran
- ORCID
- Venue
- Nature Communications
- Published
- 2020-11-12
- Volume
- 11
- Issue
- 1
- Pages
- 5749-5749
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7665028
- OpenAlex
- W3093084901
- Cited by
- 826
- References
- 100
- Citations by year
- 2026: 48 · 2025: 42 · 2024: 125 · 2023: 140 · 2022: 199 · 2021: 208 · 2020: 62 · 2019: 1
- FWCI
- 106.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (208806/Z/17/Z)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- collider bias, COVID-19 pandemic, disease severity, non-representative samples, selection bias, UK Biobank, sampling strategy, observational study design
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Humans, Pneumonia, Viral, Risk Factors, Bias, Treatment Outcome, Coronavirus Infections, Pandemics, Observational Studies as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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