We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). It is nonparametric, and selection bias under this approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies.

翻译：本文提出了一种分析流行病学中选择偏倚的新定义，该定义基于潜在结局框架。此定义既能涵盖结构性方法中的选择偏倚（即，研究中基于选择条件的调整会打开暴露与疾病之间的非因果路径，在有向无环图中体现），也能兼容传统定义（即，研究样本中观测到的关联度量与合格人群中的关联度量存在差异）。该定义为非参数方法，在零假设成立或偏离零假设的情况下，均可利用单世界干预图进行分析。该定义允许同时分析混杂偏倚与选择偏倚，明确将研究参与者的选择过程与基于研究数据的因果效应估计相关联，并可适用于描述性流行病学中的选择偏倚分析。通过实例分析，我们表明该方法为理解选择偏倚的多种生成机制提供了新视角，并简化了匹配研究及巢式病例对照研究中选择偏倚的分析过程。