Department of Applied Mathematics and Computer Sciences, Ghent University, Ghent, Belgium.
Epidemiology. 2009 Nov;20(6):851-60. doi: 10.1097/EDE.0b013e3181b6f4c9.
Estimating the effect of an exposure on an outcome, other than through some given mediator, requires adjustment for all risk factors of the mediator that are also associated with the outcome. When these risk factors are themselves affected by the exposure, then standard regression methods do not apply. In this article, I review methods for accommodating this and discuss their limitations for estimating the controlled direct effect (ie, the exposure effect when controlling the mediator at a specified level uniformly in the population). In addition, I propose a powerful and easy-to-apply alternative that uses G-estimation in structural nested models to address these limitations both for cohort and case-control studies.
估计暴露对结果的影响，而不是通过某个给定的中介，需要调整与结果相关的所有中介的风险因素。当这些风险因素本身受到暴露的影响时，那么标准回归方法就不适用了。在本文中，我回顾了适应这种情况的方法，并讨论了它们在估计受控直接效应（即在人群中统一控制中介时的暴露效应）方面的局限性。此外，我还提出了一种强大且易于应用的替代方法，该方法使用结构嵌套模型中的 G 估计来解决队列研究和病例对照研究的这些局限性。
