Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.
Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, MN, USA.
Int J Epidemiol. 2023 Dec 25;52(6):1968-1974. doi: 10.1093/ije/dyad102.
Causal directed acyclic graphs (DAGs) are often used to select variables in a regression model to identify causal effects. Outcome-based sampling studies, such as the 'test-negative design' used to assess vaccine effectiveness, present unique challenges that are not addressed by the common back-door criterion. Here we discuss intuitive, graphical approaches to explain why the common back-door criterion cannot be used for identification of population average causal effects with outcome-based sampling studies. We also describe graphical rules that can be used instead in outcome-based sampling studies when the objective is limited to determining if the causal odds ratio is identifiable, and illustrate recent changes to the free online software Dagitty which incorporate these principles.
因果有向无环图（DAG）常用于从回归模型中选择变量以确定因果效应。基于结果的抽样研究，如用于评估疫苗效力的“阴性测试设计”，提出了常见后门准则无法解决的独特挑战。在这里，我们讨论直观的图形方法，以解释为什么常见的后门准则不能用于基于结果的抽样研究中识别总体平均因果效应。我们还描述了在基于结果的抽样研究中可以替代使用的图形规则，当目标仅限于确定因果优势比是否可识别时，以及说明最近对包含这些原则的免费在线软件 Dagitty 的更改。
