Causal mediation analysis with survival data.
Causal mediation analysis with survival data.
复制标题
生存数据的因果中介分析。
DOI：
10.1097/ede.0b013e31821db37e
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
VanderWeele TJ
中科院分区：
文献类型：
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作者：
VanderWeele TJ
因果中介分析被认为是时间到事件的结果和生存分析模型。讨论了生存函数、危害、平均生存时间和中位生存量表的不同可能的效应分解。社会科学中的中介分析方法与反事实方法有关，使用加性风险、比例风险和加速失效时间模型。来自社会科学的产品系数法给出了对加性风险模型的风险差异尺度、对加速失效时间模型的对数平均生存时间差异尺度和对比例风险模型的对数风险尺度的中介效应，但仅当结果是罕见的。对于比例风险模型和共同结果，产品系数法可以为中介效应的存在提供有效的检验，但不能提供度量。当使用加性危害、加速失效时间或罕见结果比例危害模型并与反事实方法相结合时，暴露-中介相互作用可以以相对直接的方式进行调节。
Causal mediation analysis is considered for time-to-event outcomes and survival analysis models. Different possible effect decompositions are discussed for the survival function, hazard, mean survival time and median survival scales. Approaches to mediation analysis in the social sciences are related to counterfactual approaches using additive hazard, proportional hazard and accelerated failure time models. The product-coefficient method from the social sciences gives mediated effects on the hazard difference scale for additive hazard models, on the log mean survival time difference scale for accelerated failure time models, and on the log hazard scale for the proportional hazards model but only if the outcome is rare. With the proportional hazards model and a common outcome, the product-coefficient method can provide a valid test for the presence of a mediator effect but does not provide a measure. When additive hazard, accelerated failure time, or the rare-outcome proportional hazards models are employed and combined with the counterfactual approach, exposure-mediator interactions can be accommodated in a relatively straightforward manner.