Causal mediation for uncausally related mediators in the context of survival analysis.
Causal mediation for uncausally related mediators in the context of survival analysis.
复制标题
生存分析背景下非因果相关中介的因果中介。
DOI：
10.1101/2024.02.16.24302923
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Bermudez,JoseD
中科院分区：
文献类型：
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作者：
Domingo-Relloso,Arce;Jerolon,Allan;Tellez-Plaza,Maria;Bermudez,JoseD
目的研究几个变量对暴露与事件发生时间结果之间的关联的潜在中间效应是流行病学研究中感兴趣的问题。然而，据我们所知，还没有开发出工具来评估生存环境中的多个相关中介。方法在本工作中，我们将多中介算法扩展到使用半参数相加风险模型进行事件间隔时间的背景下的中介分析。我们从理论上论证了，在一定的假设条件下，间接的、直接的和总的影响可以用可折叠生存模型的反事实框架来计算。结果与结论通过模拟，我们证明了我们的算法比系数乘积方法更好，即使对于不相关的介体也是如此。附加危害模型将影响量化为比率差异，这构成了影响的衡量标准，其应用可以为公共卫生提供高度信息。我们的算法可以在Github提供的R包Multimediate中找到。
ObjectiveThe study of the potential intermediate effect of several variables on the association between an exposure and a time-to-event outcome is a question of interest in epidemiologic research. However, to our knowledge, no tools have been developed for the evaluation of multiple correlated mediators in a survival setting.MethodsIn this work, we extended the multimediate algorithm, which conducts mediation analysis in the context of multiple uncausally correlated mediators, to a time-to-event setting using the semiparametric additive hazards model. We theoretically demonstrated that, under certain assumptions, indirect, direct and total effects can be calculated using the counterfactual framework with collapsible survival models. We also adapted the algorithm to accommodate exposure-mediator interactions.Results and conclusionsUsing simulations, we demonstrated that our algorithm performs better than the product of coefficients method, even for uncorrelated mediators. The additive hazards model quantifies the effects as rate differences, which constitute a measure of impact, with applications that can be highly informative for public health. Our algorithm can be found in the R package multimediate, which is available in Github.