Institute of Statistics, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan.
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
Stat Med. 2024 Jul 20;43(16):3020-3035. doi: 10.1002/sim.10118. Epub 2024 May 21.
Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. 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, and 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.
在生物医学研究中，经常会观察到复发性事件，包括心血管事件。了解各种治疗方法对复发性事件的影响，并研究治疗方法降低复发性事件频率的潜在中介机制，这对研究人员来说是至关重要的任务。尽管已经提出了用于复发性事件数据的因果推断方法，但它们不能用于评估中介作用。本研究提出了一种新的因果中介分析方法，该方法可以在给定个体中包含感兴趣的复发性结果。在反事实框架内给出了因果估计量（直接和间接效应）的正式定义，并确定了这些效应的经验表达式。为了估计这些效应，开发了一种具有三重稳健性的半参数估计器，可以抵御模型的误设定。该方法在实际应用中得到了验证。该方法用于测量两种糖尿病药物对心血管疾病复发的影响，并检验肾脏功能在此过程中的中介作用。
