Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-death (or disease progression) through these risk factors. However, when analyzing data from a cohort study, such analyses are complicated by the longitudinal structure of the risk factors and the presence of time-varying confounders. Leveraging data from the Atherosclerosis Risk in Communities (ARIC) cohort study, we develop a causal mediation approach, using (semi-parametric) Bayesian Additive Regression Tree (BART) models for the longitudinal and survival data. Our framework allows for time-varying exposures, confounders, and mediators, all of which can either be continuous or binary. We also identify and estimate direct and indirect causal effects in the presence of a competing event. We apply our methods to assess how medication, prescribed to target cardiovascular disease (CVD) risk factors, affects the time-to-CVD death.

翻译：观察性数据的因果中介分析是研究药物通过疾病相关风险因素对死亡时间（或疾病进展）产生潜在因果效应的重要工具。然而，在分析队列研究数据时，风险因素的纵向结构以及时变混杂因子的存在使得此类分析变得复杂。利用社区动脉粥样硬化风险（ARIC）队列研究的数据，我们开发了一种因果中介分析方法，该方法采用（半参数）贝叶斯加性回归树（BART）模型处理纵向数据和生存数据。我们的框架允许时变暴露、混杂因子和中介变量，这些变量可以是连续的或二元的。我们还在存在竞争事件的情况下识别并估计了直接和间接因果效应。我们应用所提出的方法来评估针对心血管疾病（CVD）风险因素开具的药物如何影响CVD死亡时间。