Survival Bias in Mendelian Randomization Studies: A Threat to Causal Inference
Open accessAbstract
It has been argued that survival bias may distort results in Mendelian randomization studies in older populations. Through simulations of a simple causal structure we investigate the degree to which instrumental variable (IV)-estimators may become biased in the context of exposures that affect survival. We observed that selecting on survival decreased instrument strength and, for exposures with directionally concordant effects on survival (and outcome), introduced downward bias of the IV-estimator when the exposures reduced the probability of survival till study inclusion. Higher ages at study inclusion generally increased this bias, particularly when the true causal effect was not equal to null. Moreover, the bias in the estimated exposure-outcome relation depended on whether the estimation was conducted in the one- or two-sample setting. Finally, we briefly discuss which statistical approaches might help to alleviate this and other types of selection bias. See video abstract at, http://links.lww.com/EDE/B589.
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Study participation is heritable and influenced by a number of different traits, and large studies such as the UK Biobank have high levels of selection in those who participate.
“Study participation has shown to be heritable and is influenced by a number of different traits, and large studies such as the UK Biobank have been shown to have high levels of selection in those who participate.31,170–172”
From Mendelian randomization (2022) · cites this paper as [170]
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“In addition, Mendelian randomization studies have been shown to produce biased results when performed in older populations due to the increased selection caused by selective survival [62].”
Genetic risk factors for MASLD/MASH and cardiovascular disease in elderly individuals
Theel, W. B., de Jong, V., Grobbee, D. E., Jukema, J. W., Trompet, S., & Cabezas, M. C. (2025) · Annals of Hepatology · doi:10.1016/j.aohep.2025.102170 · Cited by 1 · see citing works
Self-citationDesai, R., John, A., Anderson, E. L., Stafford, J., Patel, A. M. R., Marchant, N. L., Charlesworth, G. M., Zuber, V., & Stott, J. C. H. (2025) · European Journal of Neurology · doi:10.1111/ene.70458 · Cited by 1 · see citing works
Atherosclerosis and dementia - causal aspects and preventive potential
Rasmussen, I. J., Luo, J., Rasmussen, K. L., Kjeldsen, E. W., Schneider, L. S., Sandau, N., Thomassen, J. Q., & Frikke‐Schmidt, R. (2026) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2026.120745 · Cited by 2 · see citing works
Luo, S., Ai, Y., Burgess, S., Yeung, S. L. A., & Ala‐Korpela, M. (2026) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2026.120815 · Cited by 1 · see citing works
Zhang, H., Qi, X., Kan, H., Yang, Y., & Cai, Y. (2025) · Discover Oncology · doi:10.1007/s12672-025-02159-1 · Cited by 1 · see citing works
RetractedGenetic correlation and causality of cancers and Parkinson’s diseasePreprint
Senkevich, K. A., Bandrés‐Ciga, S., Yu, E., Liyanage, U. E., Noyce, A. J., & Gan‐Or, Z. (2020) · medRxiv · doi:10.1101/2020.10.07.20208124
“Although MR can help reduce confounding and the possibility of reverse causality, a recent study demonstrated that MR is not immune to survival bias.(37) PD is mainly an elderly disease, thus it could also be prone to survival biases.”
BackgroundTian, H., Tom, B. D. M., & Burgess, S. (2023) · medRxiv · doi:10.1101/2023.10.28.23297706
Wang, Q., Shi, Q., Wang, Z., Lu, J., & Hou, J. (2023) · Figshare · doi:10.6084/m9.figshare.24223808
Desai, R., John, A., Anderson, E. L., Stafford, J., Marchant, N. L., Charlesworth, G. M., Zuber, V., & Stott, J. C. H. (2024) · medRxiv · doi:10.1101/2024.08.23.24312475
A sex-specific Mendelian randomization-phenome-wide association study of body mass index
Jiesisibieke, Z. L., Chan, I. I., Ng, J. C. M., & Schooling, C. M. (2025) · eLife · doi:10.1101/2024.09.12.24313524
“…participants are middle-aged or older, so potential recruits who did not live to recruitment because of their BMI are missing, which may bias estimates towards the null or inverse for harmful binary outcomes (Thompson et al., 2013), but less likely affects continuous outcomes (Smit et al., 2019).”
BackgroundA Sex-specific Mendelian Randomization-Phenome-Wide Association Study of Body Mass IndexPreprint
Jiesisibieke, Z. L., Chan, I. I., Ng, J. C. M., & Schooling, C. M. (2024) · eLife · doi:10.7554/elife.102573.1
A sex-specific Mendelian randomization-phenome-wide association study of body mass indexPreprint
Jiesisibieke, Z. L., Chan, I. I., Ng, J. C. M., & Schooling, C. M. (2024) · eLife · doi:10.7554/elife.102573
“This may bias estimates toward the null or inverse for harmful binary outcomes ( Thompson et al., 2013 ), but is less likely to affect continuous outcomes ( Smit et al., 2019 ).”
A Sex-specific Mendelian Randomization-Phenome-Wide Association Study of Body Mass IndexPreprint
Jiesisibieke, Z. L., Chan, I. I., Ng, J. C. M., & Schooling, C. M. (2025) · eLife · doi:10.7554/elife.102573.2
Épidémiologie descriptive et étiologique
Elbaz, A., Artaud, F., Domenighetti, C., Hajji, M., Nguyen, T. T. H., Portugal, B., & Moisan, F. (2025) · Elsevier eBooks · doi:10.1016/b978-2-294-78712-6.00002-6
Qiu, X., Li, H., Huang, W., & Liu, X. H. (2025) · Tropical Medicine & International Health · doi:10.1111/tmi.70039
“Second, MR analyses estimate the lifetime effect of exposure on disease, and causal inference is valid only if exposure does not change over time [54].”
van Vliet, N. A., Bos, M. M., Thesing, C. S., Chaker, L., Pietzner, M., Houtman, E., Neville, M. J., Li‐Gao, R., Trompet, S., Mustafa, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16921616
Self-citationTian, H., Tom, B. D. M., & Burgess, S. (2024) · Figshare · doi:10.6084/m9.figshare.26679518
Nguyen, D. A., Mera-Lojano, L. D., Nguyen, A. M., Abdelal, M., Dung, D. T. P., Duy, N. H. N., Gam, P. H., Ngọc, N. T. M., Nguyen, D. D., Thach, T. T. T. V., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8641613/v1
Kathrani, A., Brodbelt, D. C., Church, D. B., & O’Neill, D. G. (2026) · Journal of Small Animal Practice · doi:10.1111/jsap.70094
“Our study design also had elements of survival bias towards including milder cases, as only the sub-set of Pugs diagnosed before 2019 with PLE that survived into 2019 were included in the denominator study population (Smit et al., 2019).”
Insomnia, sleep duration and incident Parkinson's disease in the Finnish population cohort
Sulkava, S., Kantojärvi, K., Haukka, J. K., & Paunio, T. (2026) · Brain Communications · doi:10.1093/braincomms/fcag108
“37,38 It is thus possible that the competing risk of death weakens the connection of insomnia and Parkinson’s disease in the Mendelian randomization studies, while our cause-specific hazard analysis is not specifically vulnerable to that.”
Nguyen, D. A., Mera-Lojano, L. D., Nguyen, A. M., Abdelal, M., Dung, D. T. P., Duy, N. H. N., Gam, P. H., Ngọc, N. T. M., Nguyen, D. D., Thach, T. T. T. V., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8641613/v2
- Authors
- Smit, R. A. J., Trompet, S., Dekkers, O. M., Jukema, J. W., & le Cessie, S.
- Affiliations
- Leiden University Medical Center (NL)
- Aarhus University (DK)
- Department of Clinical Epidemiology, LUMC, Leiden, The Netherlands
- Corresponding author
- Smit, Roelof A. J.
- Venue
- Epidemiology
- Published
- 2019-11
- Volume
- 30
- Issue
- 6
- Pages
- 813-816
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC6784762
- OpenAlex
- W2965936319
- Cited by
- 83
- References
- 17
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 15 · 2023: 16 · 2022: 14 · 2021: 15 · 2020: 5
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Epidemiology (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- survivorship bias, Mendelian randomization, selection bias, older population, causal inference
- MeSH terms
- Aged, Aged, 80 and over, Computer Simulation, Humans, Bias, Selection Bias, Causality, Survival Rate, Mendelian Randomization Analysis
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