The challenge of pre-enrollment selection bias in estimating the effects of air pollution on cognitive decline and other dementia-related outcomes

BACKGROUND AND AIM: Research on air pollution’s (AP) association with dementia-related outcomes has proliferated; however, selection bias is a concern. Past research has addressed selection bias from differential post-enrollment attrition, but differential pre-enrollment selection may also be influential depending on who survives to the eligible age of enrollment and enrolls. In this project, we quantify potential biases arising from differential selection into studies of older adults through simulations based on real-world studies of AP and cognitive decline. METHODS: We simulated 1,000 cohorts of 100,000 hypothetical participants. Participants were assigned a realistic AP exposure based on prior studies, plus an unmeasured binary covariate value (U). Our simulation models specified that AP had no effect on cognitive decline, whereas U had a small positive effect. We selected 10,000 participants from each cohort to form 1,000 analytic samples, where the probability of selection depended on both AP and U. We quantified the magnitude of the resulting "collider-stratification” selection bias by comparing the average estimated effect of AP on cognitive decline across our analytic samples with the true (unbiased) value of zero. We also explored whether these biases were sensitive to AP-U interactions. RESULTS: The association between AP and cognitive decline was upwardly biased when lower AP exposure and the presence of U increased the probability of selection. The bias magnitude was particularly sensitive to multiplicative AP-U interactions. Importantly, we could replicate prior observational study findings without resorting to extreme parameter values. CONCLUSIONS: Pre-enrollment selection bias can lead to qualitatively inaccurate effect estimates in studies of older adults. These cohorts are often highly selected by virtue of their survival to eligible study age and willingness to enroll. Bias analyses (e.g., our simulation-based approach) can help identify key assumption violations that could nullify or reverse environmental effect estimates. KEYWORDS: Air pollution; Cognitive decline; Selection bias