Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
Am J Epidemiol. 2012 Sep 15;176(6):506-11. doi: 10.1093/aje/kws127. Epub 2012 Aug 17.
In a 1993 paper (Am J Epidemiol. 1993;137(1):1-8), Weinberg considered whether a variable that is associated with the outcome and is affected by exposure but is not an intermediate variable between exposure and outcome should be considered a confounder in etiologic studies. As an example, she examined the common practice of adjusting for history of spontaneous abortion when estimating the effect of an exposure on the risk of spontaneous abortion. She showed algebraically that such an adjustment could substantially bias the results even though history of spontaneous abortion would meet some definitions of a confounder. Directed acyclic graphs (DAGs) were introduced into epidemiology several years later as a tool with which to identify confounders. The authors now revisit Weinberg's paper using DAGs to represent scenarios that arise from her original assumptions. DAG theory is consistent with Weinberg's finding that adjusting for history of spontaneous abortion introduces bias in her original scenario. In the authors' examples, treating history of spontaneous abortion as a confounder introduces bias if it is a descendant of the exposure and is associated with the outcome conditional on exposure or is a child of a collider on a relevant undirected path. Thoughtful DAG analyses require clear research questions but are easily modified for examining different causal assumptions that may affect confounder assessment.
在 1993 年的一篇论文（《美国流行病学杂志》。1993；137(1):1-8）中，Weinberg 探讨了一个与结果相关且受暴露影响但不是暴露与结果之间中间变量的变量是否应被视为病因研究中的混杂因素。她以调整自发性流产史来估计暴露对自发性流产风险的影响为例，从代数上表明，这种调整即使自发性流产史符合混杂因素的某些定义，也可能严重偏倚结果。有向无环图（DAG）在几年后被引入流行病学，作为一种识别混杂因素的工具。作者现在使用 DAG 重新审视 Weinberg 的论文，以表示源自她原始假设的场景。DAG 理论与 Weinberg 的发现一致，即调整自发性流产史会导致她原始场景中的偏差。在作者的例子中，如果自发性流产史是暴露的后代，并且与暴露条件下的结果相关，或者是相关无向路径上的混杂器的后代，那么将其视为混杂因素会引入偏差。深思熟虑的 DAG 分析需要明确的研究问题，但可以轻松修改，以检查可能影响混杂因素评估的不同因果假设。
