Work

The evolution of selection bias in the recent epidemiologic literature—a selective overview

Year: 2024

Type: article

Abstract: Selection bias has long been central in methodological discussions across epidemiology and other fields. In epidemiology, the concept of selection bias has been continually evolving over time. In this issue of American Journal of Epidemiology, Mathur and Shpitser (Am J Epidemiol. 2025;194(1):267-277) present simple graphical rules for assessing the presence of selection bias when estimating causal effects by using a single-world intervention graph (SWIG). Their work is particularly insightful as it addresses the scenarios where treatment affects sample selection-a topic that has been underexplored in previous literature on selection bias. To contextualize the work by Mathur and Shpitser, we trace the evolution of the concept of selection bias in epidemiology, focusing primarily on the developments in the last 20-30 years following the adoption of causal directed acyclic graphs (DAGs) in epidemiologic research.

Source: American Journal of Epidemiology

DOI: 10.1093/aje/kwae282

Authors: Haidong Lu, Chanelle J Howe, Paul N Zivich, Gregg S Gonsalves, Daniel Westreich

Institutions: Yale University, Brown University, University of North Carolina at Chapel Hill

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute on Drug Abuse