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

• Lu, Haidong ^[1] ; Howe, Chanelle J ^[2] ; Zivich, Paul N ^[3] ; Gonsalves, Gregg S ^[1] ; Westreich, Daniel ^[3]
  1. [1] Yale School of Medicine

     Yale School of Medicine

     Town of New Haven, Estados Unidos

     
  2. [2] Brown University

     Brown University

     City of Providence, Estados Unidos

     
  3. [3] University of North Carolina at Chapel Hill

     University of North Carolina at Chapel Hill

     Township of Chapel Hill, Estados Unidos

     

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• Localización: American journal of epidemiology, ISSN-e 1476-6256, ISSN 0002-9262, Vol. 194, Nº. 3, 2025, págs. 580-584
• Idioma: inglés
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  • 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.