Vansteelandt Stijn, Vanderweele Tyler J
Department of Applied Mathematics and Computer Sciences, Ghent University, Krijgslaan 281 S9, 9000 Ghent, Belgium.
Biometrics. 2012 Dec;68(4):1019-27. doi: 10.1111/j.1541-0420.2012.01777.x. Epub 2012 Sep 18.
We define natural direct and indirect effects on the exposed. We show that these allow for effect decomposition under weaker identification conditions than population natural direct and indirect effects. When no confounders of the mediator-outcome association are affected by the exposure, identification is possible under essentially the same conditions as for controlled direct effects. Otherwise, identification is still possible with additional knowledge on a nonidentifiable selection-bias function which measures the dependence of the mediator effect on the observed exposure within confounder levels, and which evaluates to zero in a large class of realistic data-generating mechanisms. We argue that natural direct and indirect effects on the exposed are of intrinsic interest in various applications. We moreover show that they coincide with the corresponding population natural direct and indirect effects when the exposure is randomly assigned. In such settings, our results are thus also of relevance for assessing population natural direct and indirect effects in the presence of exposure-induced mediator-outcome confounding, which existing methodology has not been able to address.
我们定义了对暴露者的自然直接效应和间接效应。我们表明，与总体自然直接效应和间接效应相比，这些效应允许在较弱的识别条件下进行效应分解。当暴露不影响中介-结局关联的混杂因素时，在与受控直接效应基本相同的条件下即可进行识别。否则，若能获得关于一个不可识别的选择偏倚函数的额外知识，识别仍有可能，该函数衡量混杂因素水平内中介效应与观察到的暴露之间的依赖性，并且在一大类现实的数据生成机制中其值为零。我们认为，对暴露者的自然直接效应和间接效应在各种应用中具有内在的研究价值。此外，我们表明当暴露是随机分配时，它们与相应的总体自然直接效应和间接效应一致。因此，在这样的情况下，我们的结果对于在存在暴露引起的中介-结局混杂的情况下评估总体自然直接效应和间接效应也具有重要意义，而现有方法无法解决这一问题。
