Matching is a standard method for selecting research objects regarding the observational research, which controls confounding factors and improves statistical efficiency. However, its role in controlling confounding is not consistent in different observational studies. Matching can eliminate the confounding bias of matching variables in cohort studies, but checking on itself cannot eliminate confounding bias in case-control studies. In matched case-control studies, researchers may not accurately judge whether the variable is a confounder. Sometimes the variables that are not confounders are mistakenly matched. In that case, it will result in overmatching, which will lead to the decline of statistical efficiency or the introduction of unavoidable bias or increase of workload. If the real confounding factors are omitted, it will cause confounding bias. Therefore, researchers should consider what kind of matching variable selection criteria should be formulated. A directed acyclic graph is a visual graphic language that can show the complicated causality among different epidemiological research designs. This article analyzes the role of Matching in different observational research designs from the perspective of the directed acyclic graph, formulates the selection criteria for matching variables in matched case-control studies, and provides some reference suggestions for future epidemiological research design.
匹配是观察性研究中选择研究对象的一种常用方法，具有控制混杂因素、提高统计效率等作用，但其控制混杂因素的作用在不同观察性研究中并不一致，匹配在队列研究中能够消除匹配变量的混杂偏倚，但在病例对照研究中匹配本身并不能消除混杂偏倚。在匹配性病例对照研究选择匹配变量时，研究者可能并不能准确判断该变量是否为混杂变量，若误将真实情况为非混杂因素的变量进行匹配，则会形成过度匹配，造成统计效率下降或引入难以避免的偏倚或增加工作量等；若将真实情况为混杂因素的变量遗漏不予匹配，则会造成混杂偏倚。有向无环图是一种直观的展示不同流行病学研究设计、变量间复杂因果关系的可视化图形语言。本文从有向无环图视角分析匹配在不同观察性研究设计中的作用、匹配性病例对照研究中欲匹配变量的选择标准制定，为今后流行病学研究设计提供一定的参考建议。.