Huang Yen-Tsung, Yang Hwai-I
From the aInstitute of Statistical Science, Academia Sinica, Taipei, Taiwan; bDepartments of Epidemiology and Biostatistics, Brown University, Providence, RI; and cGenomics Research Center, Academia Sinica, Taipei, Taiwan.
Epidemiology. 2017 May;28(3):370-378. doi: 10.1097/EDE.0000000000000651.
Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multimediator models for survival outcomes is still limited.
We present methods for multimediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed-form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram.
Mediation analyses using Cox models under the rare-outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time and survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load.
Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.
中介分析一直是一种通过中介来研究暴露对结局影响的常用方法。针对连续和二分结局，已经提出了具有多个中介的中介模型。然而，用于生存结局的多中介模型的发展仍然有限。
我们提出了使用三种生存模型进行多中介分析的方法：阿伦累加风险模型、考克斯比例风险模型和半参数概率单位模型。通过中介的效应可以用特定路径效应来表征，并给出了其定义和可识别性假设。我们推导了这三种模型特定路径效应的封闭形式表达式，并使用因果图进行直观解释。
在罕见结局假设下使用考克斯模型和阿伦累加风险模型的中介分析分别考虑对对数风险比和风险差异的影响；使用半参数概率单位模型的分析考虑对转换后生存时间和生存概率差异的影响。这三种模型应用于一项肝炎研究，我们研究了丙型肝炎通过基线和/或随访乙型肝炎病毒载量对肝癌发病率的影响。这三种方法在各自的效应尺度上显示出一致的结果，这表明丙型肝炎对肝癌有未通过乙型肝炎介导的不良估计效应，以及通过乙型肝炎病毒载量基线（可能还有随访）介导的保护估计效应。
针对累加风险、比例风险和概率单位模型，开发了具有多个中介的生存结局的因果中介分析，并在一项肝炎研究中证明了其效用。
