Observational cohort data is an important source of information for understanding the causal effects of treatments on survival and the degree to which these effects are mediated through changes in disease-related risk factors. However, these analyses are often complicated by irregular data collection intervals and the presence of longitudinal confounders and mediators. We propose a causal mediation framework that jointly models longitudinal exposures, confounders, mediators, and time-to-event outcomes as continuous functions of age. This framework for longitudinal covariate trajectories enables statistical inference even at ages where the subject's covariate measurements are unavailable. The observed data distribution in our framework is modeled using an enriched Dirichlet process mixture (EDPM) model. Using data from the Atherosclerosis Risk in Communities cohort study, we apply our methods to assess how medication -- prescribed to target cardiovascular disease (CVD) risk factors -- affects the time-to-CVD death.

翻译：观察性队列数据是理解治疗对生存的因果效应以及这些效应在多大程度上通过疾病相关风险因素的变化而中介的重要信息来源。然而，这类分析常因数据采集间隔不规律以及存在纵向混杂因素和中介变量而变得复杂。我们提出一种因果中介分析框架，将纵向暴露、混杂因素、中介变量和事件发生时间结局联合建模为年龄的连续函数。该纵向协变量轨迹框架能在受试者协变量测量值缺失的年龄进行统计推断。我们使用富集狄利克雷过程混合(EDPM)模型对框架中的观测数据分布进行建模。基于社区动脉粥样硬化风险队列研究数据，我们应用该方法评估针对心血管疾病风险因素处方的药物如何影响心血管疾病死亡时间。