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

Abstract

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 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.

Conclusion: 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.

Keywords: collider-stratification bias; competing events; estimands; generalizability; health disparities; selection bias.

Figure 1.

Directed acyclic graphs representing selection mechanisms. R represents the social groups to be contrasted or an exposure/intervention around a social mechanism that creates or targets health disparities across social groups. Y represents the outcome of interest. In graphs A and B, S represents selection (inclusion) into the initial sample or into remaining in the sample in longitudinal studies. In graphs A and B, L1 represents shared causes of S and Y. In graph A, this results in different distributions of L1 in the sample and target population, which threatens generalizability. In graph B, where R influences selection into the sample, S becomes a collider and conditioning on S = 1 creates an indirect pathway that induces a spurious association between R and Y. Graph C represents a scenario where D is death, a competing event that precludes the outcome of interest. The arrow between D and Y represents a special feature of competing events: when D = 1, probability of Y is zero at future time points. In this scenario, L2 represents shared causes of D and Y. Depending on the target estimand, the indirect pathway mediated by death either represents a mechanism included in the estimand (for a total effect), or a mechanism that induces selection bias (for a direct effect).