Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Background: The process of creating a cohort or cohort sub -study may induce misleading exposure -health effect associations through collider stratification bias (i.e. selection bias) or bias due to co nditioning on an intermediate. Studies of environmental risk factors may be at particular risk. Objectives: To demonstrate how such bias es of the exposure -health effect association arise and how one may mitigate them. Methods: We used directed acyclic graphs and the example of bone lea d and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead sub-study . We then applied methods (adjustment , restriction, and inverse probability of attrition weighting) to mitigate the se bias es in analyses using C ox proportional hazards models to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI). Results: A nalyses adjusted for age at bon e lead measurement, smoking, and education among all men found HRs (95% CI) for the highest vs. lowest tertile of patella lead of 1.34 (0.90-2.00), 1.46 (0.86-2.48), and 2.01 (0.86-4.68) for all-cause, cardiovascular, and ischemic heart di sease mortality, respectively. After applying methods to mitigate the bias es, the HR (95% CI) among the 63 7 men analyzed were 1.86 (1.12-3.09), 2.47 (1.23-4.96), and 5.20 (1.61-16.8), respectively. Conclusions: Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples, and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls.