Lu Haidong, Howe Chanelle J, Zivich Paul N, Gonsalves Gregg S, Westreich Daniel
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.
Am J Epidemiol. 2025 Mar 4;194(3):580-584. doi: 10.1093/aje/kwae282.
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.
长期以来，选择偏倚一直是流行病学和其他领域方法学讨论的核心。在流行病学中，选择偏倚的概念随着时间不断演变。在本期《美国流行病学杂志》中，马图尔和什皮采尔（《美国流行病学杂志》。2025年；194(1):267 - 277）提出了简单的图形规则，用于在使用单世界干预图（SWIG）估计因果效应时评估选择偏倚的存在。他们的工作特别有见地，因为它解决了治疗影响样本选择的情况——这一主题在以往关于选择偏倚的文献中探讨较少。为了将马图尔和什皮采尔的工作置于背景中，我们追溯了流行病学中选择偏倚概念的演变，主要关注在流行病学研究中采用因果有向无环图（DAGs）之后过去20 - 30年的发展情况。
