Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?

Purpose of Review To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research. Recent Findings Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias is a threat 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. Summary 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.