Ikram M Arfan, VanderWeele Tyler J
Departments of Epidemiology, Neurology, and Radiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
Departments of Epidemiology and Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
Eur J Epidemiol. 2015 Oct;30(10):1115-8. doi: 10.1007/s10654-015-0087-5. Epub 2015 Oct 5.
Understanding of causal pathways in epidemiology involves the concepts of direct and indirect effects. Recently, causal mediation analysis has been formalized to quantify these direct and indirect effects in the presence of exposure-mediator interaction and even allows for four-way decomposition of the total effect: controlled direct effect, reference interaction, mediated interaction, pure indirect effect. Whereas the other three effects can be intuitively conceptualized, mediated interaction is often considered a nuisance in statistical analysis. In this paper, we focus on mediated interaction and contrast it against pure mediation. We also propose a clinical and biological interpretation of mediated interaction using three hypothetical examples. With these examples we aim to make researchers aware that mediated interaction can actually provide important clinical and biological information.
对流行病学中因果途径的理解涉及直接效应和间接效应的概念。最近，因果中介分析已被形式化，以在存在暴露-中介相互作用的情况下量化这些直接和间接效应，甚至允许对总效应进行四重分解：受控直接效应、参考相互作用、中介相互作用、纯间接效应。虽然其他三种效应可以直观地概念化，但中介相互作用在统计分析中通常被视为一种干扰因素。在本文中，我们专注于中介相互作用，并将其与纯中介进行对比。我们还使用三个假设示例对中介相互作用提出了临床和生物学解释。通过这些示例，我们旨在让研究人员意识到中介相互作用实际上可以提供重要的临床和生物学信息。
