IDENTIFIABILITY AND EXCHANGEABILITY FOR DIRECT AND INDIRECT EFFECTS
IDENTIFIABILITY AND EXCHANGEABILITY FOR DIRECT AND INDIRECT EFFECTS
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DOI：
10.1097/00001648-199203000-00013
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发表时间：
1992-03-01
期刊：
影响因子：
5.4
通讯作者：
GREENLAND, S
中科院分区：
文献类型：
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作者：
ROBINS, JM;GREENLAND, S
我们考虑将暴露的直接影响与通过中间变量传递的影响（间接影响）分开的问题。我们表明，对中间变量的调整（这是估计直接影响的最常见方法）可能会出现偏差。我们还表明，即使在随机交叉暴露试验中，如果没有特殊假设，也无法区分直接影响和间接影响；换句话说，当仅随机暴露时，直接和间接影响是无法单独识别的。如果暴露和中间效应永远不会相互作用而导致疾病，并且中间效应可以被控制，即通过适当的干预措施来阻止，那么随机化暴露和干预的试验可以将直接效应与间接效应区分开来。尽管如此，估计必须使用G计算算法来进行。传统的调整方法仍然存在偏差。当暴露和中间效应相互作用导致疾病时，即使在随机分配暴露和阻断中间效应的干预的试验中，直接效应和间接效应也不能分开。尽管如此，在这样的试验中，人们仍然可以估计可以通过控制中间体来预防的暴露诱发疾病的比例。即使没有阻止中间效应的干预措施，如果获得了其他混杂变量的数据，也可以通过 G 计算算法估计可以通过控制中间效应来预防的暴露诱发疾病的比例。
We consider the problem of separating the direct effects of an exposure from effects relayed through an intermediate variable (indirect effects). We show that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased. We also show that, even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions; in other words, direct and indirect effects are not separately identifiable when only exposure is randomized. If the exposure and intermediate never interact to cause disease and if intermediate effects can be controlled, that is, blocked by a suitable intervention, then a trial randomizing both exposure and the intervention can separate direct from indirect effects. Nonetheless, the estimation must be carried out using the G-computation algorithm. Conventional adjustment methods remain biased. When exposure and the intermediate interact to cause disease, direct and indirect effects will not be separable even in a trial in which both the exposure and the intervention blocking intermediate effects are randomly assigned. Nonetheless, in such a trial, one can still estimate the fraction of exposure-induced disease that could be prevented by control of the intermediate. Even in the absence of an intervention blocking the intermediate effect, the fraction of exposure-induced disease that could be prevented by control of the intermediate can be estimated with the G-computation algorithm if data are obtained on additional confounding variables.