Petersen Maya L, Sinisi Sandra E, van der Laan Mark J
Division of Biostatistics, University of California, School of Public Health, Berkeley, California 94720-7360, USA.
Epidemiology. 2006 May;17(3):276-84. doi: 10.1097/01.ede.0000208475.99429.2d.
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 intermediate 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).
流行病学和临床研究中的许多常见问题都涉及在阻断暴露对中间变量影响的同时，估计暴露对结局的效应。这类效应被称为直接效应。直接效应的估计通常是旨在理解暴露导致或预防疾病的机制途径的研究目标，在许多其他情况下也是如此。尽管多变量回归通常用于估计直接效应，但这种方法需要的假设超出了估计总因果效应所需的假设。此外，当暴露和中间变量相互作用导致疾病时，多变量回归估计的是一种特定类型的直接效应——当中间变量固定在指定水平时，暴露对结局的效应。使用反事实框架，我们将这种直接效应的定义（受控直接效应）与另一种定义区分开来，在另一种定义中，暴露对中间变量的效应被阻断，但中间变量在其他方面被允许像在无暴露情况下那样变化（自然直接效应）。我们通过几个例子来说明受控直接效应和自然直接效应之间的差异。我们提出了一种可以使用标准统计软件实施的自然直接效应估计方法，并回顾了我们方法所基于的假设（这些假设比先前作者提出的假设限制更少）。
