UNASSIGNED: To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research.
UNASSIGNED: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.
UNASSIGNED: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.

Bias due to selective mortality is a potential concern in many studies and is especially relevant in cognitive aging research because cognitive impairment strongly predicts subsequent mortality. Biased estimation of the effect of an exposure on rate of cognitive decline can occur when mortality is a common effect of exposure and an unmeasured determinant of cognitive decline and in similar settings. This potential is often represented as collider-stratification bias in directed acyclic graphs, but it is difficult to anticipate the magnitude of bias. In this paper, we present a flexible simulation platform with which to quantify the expected bias in longitudinal studies of determinants of cognitive decline. We evaluated potential survival bias in naive analyses under several selective survival scenarios, assuming that exposure had no effect on cognitive decline for anyone in the population. Compared with the situation with no collider bias, the magnitude of bias was higher when exposure and an unmeasured determinant of cognitive decline interacted on the hazard ratio scale to influence mortality or when both exposure and rate of cognitive decline influenced mortality. Bias was, as expected, larger in high-mortality situations. This simulation platform provides a flexible tool for evaluating biases in studies with high mortality, as is common in cognitive aging research.

To estimate the possible direct effect of birth weight on blood pressure, it is conventional to condition on the mediator, current weight. Such conditioning can induce bias. Our aim was to assess the potential biasing effect of U, an unmeasured common cause of current weight and blood pressure, on the estimate of the controlled direct effect of birth weight on blood pressure, with the help of sensitivity analyses. We used data from a school-based study conducted in Switzerland in 2005-2006 (n = 3,762; mean age = 12.3 years). A small negative association was observed between birth weight and systolic blood pressure (linear regression coefficient βbw = -0.3 mmHg/kg, 95% confidence interval: -0.9, 0.3). The association was strengthened upon adjustment for current weight (βbw|C = -1.5 mmHg/kg, 95% confidence interval: -2.1, -0.9). Sensitivity analyses revealed that the negative conditional association was explained by U only if U was relatively strongly associated with blood pressure and if there was a large difference in the prevalence of U between low-birth weight and normal-birth weight children. This weakens the hypothesis that the negative relationship between birth weight and blood pressure arises only from collider-stratification bias induced by conditioning on current weight.