Luo T, Wang L, Tian T, Fu W H, Pei H L, Zheng Y J, Dai J H
Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi 830011, China.
Department of Epidemiology, Key Laboratory for Health Technology Assessment, National Health Commission, Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2021 Apr 10;42(4):740-744. doi: 10.3760/cma.j.cn112338-20200601-00793.
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标准方法，它可以控制混杂因素并提高统计效率。然而，其在控制混杂方面的作用在不同的观察性研究中并不一致。匹配可以消除队列研究中匹配变量的混杂偏倚，但自身检验并不能消除病例对照研究中的混杂偏倚。在匹配的病例对照研究中，研究人员可能无法准确判断某个变量是否为混杂因素。有时非混杂因素的变量会被错误匹配。在这种情况下，会导致过度匹配，进而导致统计效率下降或引入不可避免的偏倚或增加工作量。如果遗漏了真正的混杂因素，则会导致混杂偏倚。因此，研究人员应考虑应制定何种匹配变量选择标准。有向无环图是一种可视化图形语言，能够展示不同流行病学研究设计之间复杂的因果关系。本文从有向无环图的角度分析匹配在不同观察性研究设计中的作用，制定匹配病例对照研究中匹配变量的选择标准，并为未来的流行病学研究设计提供一些参考建议。
