Loss to follow-up in cohort studies: how much is too much?
Abstract
Loss to follow-up is problematic in most cohort studies and often leads to bias. Although guidelines suggest acceptable follow-up rates, the authors are unaware of studies that test the validity of these recommendations. The objective of this study was to determine whether the recommended follow-up thresholds of 60-80% are associated with biased effects in cohort studies. A simulation study was conducted using 1000 computer replications of a cohort of 500 observations. The logistic regression model included a binary exposure and three confounders. Varied correlation structures of the data represented various levels of confounding. Differing levels of loss to follow-up were generated through three mechanisms: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). The authors found no important bias with levels of loss that varied from 5 to 60% when loss to follow-up was related to MCAR or MAR mechanisms. However, when observations were lost to follow-up based on a MNAR mechanism, the authors found seriously biased estimates of the odds ratios with low levels of loss to follow-up. Loss to follow-up in cohort studies rarely occurs randomly. Therefore, when planning a cohort study, one should assume that loss to follow-up is MNAR and attempt to achieve the maximum follow-up rate possible.
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Participants rarely leave a trial due to random reasons.
“because participants rarely leave a trial due to random reasons [25].”
From Patient Engagement with Conversational Agents in Health Applications 2016–2022: A Systematic Review and Meta-Analysis (2024) · cites this paper as [25]
Loss of data of more than 20% can have a profound impact on the validity of a study, if this loss is not of a random nature.
“Loss of data of more than 20% can have a profound impact on the validity of a study, if this loss is not of a random nature (Kristman et al., 2004).”
From The relationship between improvement in insomnia severity and long-term outcomes in the treatment of chronic fatigue (2018) · cites this paper as Kristman et al., 2004
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- Authors
- Kristman, V., Manno, M., & Côté, P.
- Affiliations
- University of Toronto (CA)
- Mount Sinai Hospital (CA)
- Venue
- European Journal of Epidemiology
- Published
- 2004-08
- Volume
- 19
- Issue
- 8
- Pages
- 751-760
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- OpenAlex
- W2013188809
- Cited by
- 740
- References
- 21
- Citations by year
- 2026: 31 · 2025: 51 · 2024: 50 · 2023: 50 · 2022: 68 · 2021: 77 · 2020: 52 · 2019: 47 · 2018: 57 · 2017: 55 · 2016: 39 · 2015: 28 · 2014: 21 · 2013: 30 · 2012: 17
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- loss to follow-up, cohort studies, follow-up rates, missing data mechanisms, missing not at random, missing at random, missing completely at random, selection bias, confounding, simulation study, odds ratio bias
- MeSH terms
- Canada, Epidemiologic Methods, Follow-Up Studies, Humans, Sensitivity and Specificity, Cohort Studies, Bias, Confounding Factors, Epidemiologic, Confidence Intervals, Odds Ratio
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