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理解干预效果:中介分析的更自然方法?

Understanding Interventional Effects: A More Natural Approach to Mediation Analysis?

机构信息

From the Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.

出版信息

Epidemiology. 2018 Sep;29(5):614-617. doi: 10.1097/EDE.0000000000000866.

Abstract

The causal mediation literature has mainly focused on "natural effects" as measures of mediation, but these have been criticized for their reliance on empirically unverifiable assumptions. They are also impossible to estimate without additional untestable assumptions in the common situation of exposure-induced mediator-outcome confounding. "Interventional effects" have been proposed as alternative measures that overcome these limitations, and 2 versions have been described for the exposure-induced confounding problem. We aim to provide insight into the interpretation of these effects, particularly by describing randomized controlled trials that could hypothetically be conducted to estimate them. In contrast with natural effects, which are defined in terms of individual-level interventions, the definitions of interventional effects rely on population-level interventions. This distinction underpins the previously described advantages of interventional effects, and reflects a shift from individual effects to more tangible population-average effects. We discuss the conceptual and practical implications for the conduct of mediation analysis. See video abstract at, http://links.lww.com/EDE/B383.

摘要

因果中介文献主要关注作为中介衡量的“自然效应”,但这些效应因其依赖于经验上无法验证的假设而受到批评。在常见的暴露引起的中介结果混杂情况下,没有额外无法检验的假设,这些效应也是不可能估计的。“干预效应”已被提议作为克服这些限制的替代措施,并针对暴露引起的混杂问题描述了 2 种版本。我们旨在深入了解这些效应的解释,特别是通过描述可以假设进行以估计这些效应的随机对照试验。与自然效应不同,自然效应是根据个体水平的干预来定义的,干预效应的定义依赖于人群水平的干预。这种区别是先前描述的干预效应的优势的基础,反映了从个体效应到更具体的人群平均效应的转变。我们讨论了进行中介分析的概念和实际影响。在,http://links.lww.com/EDE/B383 观看视频摘要。

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