Matched designs and causal diagrams
Matched designs and causal diagrams
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DOI：
10.1093/ije/dyt083
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发表时间：
2013-06-01
影响因子：
7.7
通讯作者：
Greenland, Sander
中科院分区：
文献类型：
--
作者：
Mansournia, Mohammad A.;Hernan, Miguel A.;Greenland, Sander
我们使用因果关系图来说明匹配的后果和队列和病例对照研究中匹配变量的适当处理。匹配过程通常会迫使某些变量成为独立的，尽管它们在因果图中是相连的，这种现象被称为不忠。我们展示了如何因果图可以用来可视化许多以前的结果匹配的研究。队列匹配可以防止匹配变量的混杂，但删失或其他缺失数据和进一步调整可能需要控制匹配变量。病例对照匹配通常不能防止匹配变量的混杂，即使最初不是混杂因素，也可能需要控制匹配变量。匹配受暴露和结果影响的变量，或暴露和结果之间的中间变量，通常会产生不可补救的偏差。
We use causal diagrams to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies. The matching process generally forces certain variables to be independent despite their being connected in the causal diagram, a phenomenon known as unfaithfulness. We show how causal diagrams can be used to visualize many previous results about matched studies. Cohort matching can prevent confounding by the matched variables, but censoring or other missing data and further adjustment may necessitate control of matching variables. Case-control matching generally does not prevent confounding by the matched variables, and control of matching variables may be necessary even if those were not confounders initially. Matching on variables that are affected by the exposure and the outcome, or intermediates between the exposure and the outcome, will ordinarily produce irremediable bias.