Estimation of direct causal effects
Estimation of direct causal effects
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DOI：
10.1097/01.ede.0000208475.99429.2d
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发表时间：
2006-05-01
期刊：
影响因子：
5.4
通讯作者：
van der Laan, MJ
中科院分区：
文献类型：
--
作者：
Petersen, ML;Sinisi, SE;van der Laan, MJ
流行病学和临床研究中的许多常见问题涉及估计暴露对结果的影响，同时阻止暴露对中间变量的影响。这种影响称为直接影响。对直接影响的估计通常是研究的目标，目的是了解暴露导致或预防疾病的机制途径，以及在许多其他环境中。虽然多变量回归通常被用来估计直接影响，但这种方法需要的假设超出了估计总因果影响所需的假设。此外，当暴露和中间变量相互作用导致疾病时，多变量回归估计特定类型的直接影响--当中间变量固定在特定水平时，暴露对结果的影响。利用反事实框架，我们将直接影响(受控直接影响)的这一定义与另一种定义区分开来，在另一种定义中，暴露对中间体的影响被阻止，但中间体被允许改变，就像在没有暴露的情况下一样(自然直接影响)。我们用几个例子说明了受控直接效应和自然直接效应之间的区别。我们提出了一种可以使用标准统计软件实现的自然直接影响的估计方法，并回顾了我们方法背后的假设(与先前作者提出的假设相比，这些假设的限制性较低)。
Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate variables interact to cause disease, multivariable regression estimates a particular type of direct effect-the effect of an exposure on an outcome when the intermediate is fixed at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (controlled direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the inter-mediate is otherwise allowed to vary as it would in the absence of exposure (natural direct effect). We illustrate the difference between controlled and natural direct effects using several examples. We present an estimation approach for natural direct effects that can be implemented using standard statistical software, and we review the assumptions underlying our approach (which are less restrictive than those proposed by previous authors).