Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Researchers must understand the effects of various treatments on recurrent events and investigate the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real-world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process.

翻译：复发事件（包括心血管事件）在生物医学研究中普遍存在。研究人员必须理解不同治疗方案对复发事件的影响，并探究治疗可能降低复发事件频率的潜在中介机制，这一点至关重要。尽管已有针对复发事件数据的因果推断方法被提出，但这些方法无法用于评估中介效应。本研究提出了一种新颖的因果中介分析方法论，能够处理特定个体中关注的复发结局。在反事实框架内给出了因果估计量（直接效应与间接效应）的形式化定义，并识别了这些效应的经验表达式。为估计这些效应，开发了具有三重鲁棒性（对模型误设）的半参数估计量。通过实际应用验证了所提出方法论的有效性。该方法被用于衡量两种糖尿病药物对心血管疾病复发的影响，并检验肾功能在此过程中的中介作用。