A framework for understanding selection bias in real-world healthcare data.

ABSTRACT: Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We

SUBMITTER: Kundu R 

PROVIDER: S-EPMC11393555 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature