Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA 02115, USA.
Epidemiology. 2010 Jul;21(4):540-51. doi: 10.1097/EDE.0b013e3181df191c.
A key question in many studies is how to divide the total effect of an exposure into a component that acts directly on the outcome and a component that acts indirectly, ie, through some intermediate. For example, one might be interested in the extent to which the effect of diet on blood pressure is mediated through sodium intake and the extent to which it operates through other pathways. In the context of such mediation analysis, even if the effect of the exposure on the outcome is unconfounded, estimates of direct and indirect effects will be biased if control is not made for confounders of the mediator-outcome relationship. Often data are not collected on such mediator-outcome confounding variables; the results in this paper allow researchers to assess the sensitivity of their estimates of direct and indirect effects to the biases from such confounding. Specifically, the paper provides formulas for the bias in estimates of direct and indirect effects due to confounding of the exposure-mediator relationship and of the mediator-outcome relationship. Under some simplifying assumptions, the formulas are particularly easy to use in sensitivity analysis. The bias formulas are illustrated by examples in the literature concerning direct and indirect effects in which mediator-outcome confounding may be present.
许多研究中的一个关键问题是如何将暴露的总效应分解为直接作用于结果的部分和间接作用的部分，即通过某些中间变量。例如，人们可能会关注饮食对血压的影响在多大程度上是通过钠摄入介导的，以及在多大程度上是通过其他途径起作用的。在这种中介分析的背景下，即使暴露对结果的影响没有混杂，但若不对中介-结果关系的混杂因素进行控制，直接和间接效应的估计也会存在偏差。通常情况下，不会收集到关于这种中介-结果混杂变量的数据；本文的结果允许研究人员评估他们对直接和间接效应估计的偏差对这种混杂的敏感性。具体来说，本文提供了由于暴露-中介关系和中介-结果关系的混杂而导致直接和间接效应估计偏差的公式。在一些简化假设下，这些公式在敏感性分析中特别容易使用。偏倚公式通过文献中关于直接和间接效应的示例来说明，其中可能存在中介-结果混杂。
