Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. Despite the extensive existing literature, there lacks generally applicable and accessible methods for principal stratification analysis with time-to-event outcomes. In this paper, we specify two causal estimands for time-to-event outcomes in principal stratification. For estimation, we adopt the general strategy of latent mixture modeling and derive the corresponding likelihood function. For computational convenience, we illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters via the Hamiltonian Monte Carlo. We provide the analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE trial to evaluate the causal effect of taking 81 mg versus 325 mg aspirin on the risk of major adverse cardiovascular events.
翻译:在临床试验中,常见的情况是治疗不合规和因终期事件而审查等突发事件。主要分层是发生突发事件时因果推断的一个框架。尽管现有大量文献,但缺乏普遍适用和可获得的主要分层分析方法以及时间到活动结果。在本文件中,我们为主要分层中的时间到活动结果指定了两个因果估计值。关于估计,我们采用潜在混合模型的一般战略,并得出相应的概率函数。关于计算方便性,我们用巴伊西亚参数 Weibull-Cox比例模型混合说明总体战略。我们使用斯坦编程语言通过汉密尔顿蒙特卡洛号对模型参数进行自动的海面取样。我们提供了因果估计表的分析形式,作为模型参数的功能,并在没有分析形式时采用替代的数字方法。我们将拟议方法应用于ADAPTEtable试验,以评价对主要心血管危险采用81毫克至325毫克阿司匹林的因果关系。我们使用Stan编程语言对主要心血管危险进行了评估。