Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders
Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders
复制标题
DOI：
10.1002/sim.7426
复制
发表时间：
2017-11-20
影响因子：
2
通讯作者：
VanderWeele, Tyler J.
中科院分区：
文献类型：
--
作者：
Lin, Sheng-Hsuan;Young, Jessica G.;VanderWeele, Tyler J.
我们提出了一种方法来进行中介分析的生存数据随时间变化的曝光，介质和混杂因素。我们确定某些干预的直接和间接影响，通过生存中介g公式，并描述所需的假设。我们还提供了一个可行的参数方法沿着的算法和软件来估计这些影响。我们应用此方法分析心脏研究的数据，以探讨吸烟对冠心病死亡率的因果机制。“每天总是吸烟30支”与“从不吸烟”相比，估计的10年全因死亡风险差异为4.3（95% CI =（1.37，6.30））。在总体效应中，我们估计7.91%（95%CI：= 1.36%，19.32%）由冠状动脉疾病的发生率和时间介导。生存中介g-公式是进行纵向数据中介分析的有力工具。
We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The estimated overall 10-year all-cause mortality risk difference comparing "always smoke 30 cigarettes per day" versus "never smoke" was 4.3 (95% CI = (1.37, 6.30)). Of the overall effect, we estimated 7.91% (95% CI: = 1.36%, 19.32%) was mediated by the incidence and timing of coronary artery disease. The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data.