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Treffer: The evolution of selection bias in the recent epidemiologic literature-a selective overview.

Title:
The evolution of selection bias in the recent epidemiologic literature-a selective overview.
Authors:
Lu H; Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, United States.; Program in Addiction Medicine, Yale School of Medicine, New Haven, CT 06510, United States.; Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06511, United States., Howe CJ; Center for Epidemiologic Research, Department of Epidemiology, School of Public Health, Brown University, Providence, RI 02903, United States., Zivich PN; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States., Gonsalves GS; Program in Addiction Medicine, Yale School of Medicine, New Haven, CT 06510, United States.; Public Health Modeling Unit, Yale School of Public Health, New Haven, CT 06511, United States.; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, United States., Westreich D; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Source:
American journal of epidemiology [Am J Epidemiol] 2025 Mar 04; Vol. 194 (3), pp. 580-584.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 7910653 Publication Model: Print Cited Medium: Internet ISSN: 1476-6256 (Electronic) Linking ISSN: 00029262 NLM ISO Abbreviation: Am J Epidemiol Subsets: MEDLINE
Imprint Name(s):
Publication: Cary, NC : Oxford University Press
Original Publication: Baltimore, School of Hygiene and Public Health of Johns Hopkins Univ.
MeSH Terms:
Epidemiologic Studies*, Selection Bias ; Humans ; Causality ; Epidemiologic Methods
Comments:
Comment in: Am J Epidemiol. 2025 Nov 4;194(11):3396-3397. doi: 10.1093/aje/kwaf174.. (PMID: 40838589)
References:
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Grant Information:
K99 DA057487 United States DA NIDA NIH HHS; K99DA057487 United States NH NIH HHS
Contributed Indexing:
Keywords: causal directed acyclic graph; causal inference; collider bias; epidemiologic research; selection bias; single-world intervention graph
Entry Date(s):
Date Created: 20240813 Date Completed: 20250423 Latest Revision: 20251120
Update Code:
20251121
PubMed Central ID:
PMC11879605
DOI:
10.1093/aje/kwae282
PMID:
39136207
Database:
MEDLINE

Weitere Informationen

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.
(© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)