有向无环图在混杂因素识别与控制中的应用及实例分析

观察性研究是流行病学病因研究常用的研究设计,但应用观察性研究进行因果推断时,常由于未经识别、校正的混杂因素的存在,歪曲暴露因素与研究结局之间的真实因果关系。传统混杂因素判断标准在实际应用中不够直观,且有一定局限性,有时甚至出现混杂因素的误判。有向无环图(DAGs)可以直观识别观察性研究中存在的混杂因素,将复杂的因果关系可视化,判断研究中需要校正的最小校正子集,并可避免传统混杂因素判断标准的局限性,结合DAGs还可以指导混杂因素校正方法的选择,在观察性研究中因果推断具有重要指导价值,DAGs在未来的流行病学研究中将有更多的应用。

Application of directed acyclic graphs in identifying and controlling confounding bias

Observational study has been viewed as the most convenient method in designing etiological studies. However, the presence of confounders always challenge the researchers in study design, since unadjusted confounders may lead to biased results. The traditional definition of a confounder is not intuitional in application and sometimes leading to inappropriate adjustment of nonexistent "confounders" which might induce new bias to merge. The use of directed acyclic graphs (DAGs) may identify confounders easier and more intuitional, as well as avoiding superfluous adjustment. It can also contribute to the identification of adjustment methods, and be useful in causal inference of observational studies.