Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Int J Epidemiol. 2013 Jun;42(3):860-9. doi: 10.1093/ije/dyt083.
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.
我们使用因果图来说明匹配的后果，以及在队列研究和病例对照研究中如何正确处理匹配变量。匹配过程通常会导致某些变量尽管在因果图中是相关的，但在实际上却是独立的，这种现象被称为不忠实。我们展示了如何使用因果图来可视化许多关于匹配研究的先前结果。队列匹配可以防止匹配变量引起的混杂，但删失或其他缺失数据以及进一步的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引起的混杂，即使这些变量最初不是混杂因素，也可能需要控制匹配变量。在暴露和结局，或暴露和结局之间的中间变量上进行匹配，通常会产生不可挽回的偏差。
