Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Stat Med. 2019 Apr 30;38(9):1566-1581. doi: 10.1002/sim.8058. Epub 2018 Dec 18.
Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. However, studies regarding mediation effects on survival outcomes are limited, particularly in multi-mediator settings. The existing multi-mediator analyses for survival outcomes are either performed under special model specifications such as probit models or additive hazard models, or they assume a rare outcome. Here, we propose a novel multi-mediation analysis based on the widely used Cox proportional hazards model without the rare outcome assumption. We develop a methodology under a counterfactual framework to identify path-specific effects (PSEs) of the exposure on the outcome through the mediator(s) and derive the closed-form formula for PSEs on a transformed survival time. Moreover, we show that the convolution of an extreme value and Gaussian random variables converges to another Gaussian, provided that the variance of the original Gaussian gets large. Based on that, we further derive closed-form expressions for PSEs on survival probabilities. Asymptotic properties are established for both estimators. Extensive simulation is conducted to evaluate the finite sample performance of our proposed estimators and to compare with existing methods. The utility of the proposed method is illustrated in a hepatitis study of liver cancer risk.
因果中介分析旨在研究暴露与结局之间的关联机制。然而，关于生存结局中介效应的研究有限，特别是在多中介情况下。现有的生存结局多中介分析要么在特殊模型规范下进行，如概率模型或加性风险模型，要么假设结局罕见。在这里，我们提出了一种新的基于广泛使用的 Cox 比例风险模型的多中介分析方法，无需罕见结局假设。我们在反事实框架下开发了一种方法，通过中介来识别暴露对结局的路径特定效应（PSE），并推导出在转换生存时间上的 PSE 的闭式公式。此外，我们表明，极端值和高斯随机变量的卷积在原始高斯的方差较大的情况下收敛于另一个高斯。基于此，我们进一步推导出生存概率上的 PSE 的闭式表达式。我们为两个估计量建立了渐近性质。通过广泛的模拟，评估了我们提出的估计量的有限样本性能，并与现有方法进行了比较。在肝癌风险的肝炎研究中说明了所提出方法的实用性。
