Loss to Follow-Up in Cohort Studies: How Much is Too Much?
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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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- Loss to Follow-Up in Cohort Studies: How Much is Too Much?
European Journal of Epidemiology
Volume 19, Issue 8 , pp 751-760
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- Bias (epidemiology)
- Cohort studies
- Computer simulation
- Epidemiologic methods
- Follow-up studies
- Logistic models
- Industry Sectors
- Author Affiliations
- 1. Institute for Work & Health; Department of Public Health Sciences, University of Toronto, 481 University Ave., Suite 800, Toronto, Ontaria, Canada M5G 2E9,Phone: +1 (416) 927-2027, ext. 2157; Fax: +1 (416) 927-4167; E-mail: firstname.lastname@example.org
- 2. Department of Public Health Sciences, University of Toronto, Samuel Lunenfeld Research Institute Mount Sinai Hospital, Toronto, Ontario, Canada