In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are generally interested in going beyond the Intention-To-Treat (ITT) analysis, which provides valid causal estimates of the effect of treatment assignment but does not inform on the effect of the actual treatment receipt. Our study is motivated by an RCT in oncology, where patients assigned the investigational treatment may discontinue it due to adverse events. We propose adopting a principal stratum strategy and decomposing the overall ITT effect into principal causal effects for groups of patients defined by their potential discontinuation behavior. We first show how to implement a principal stratum strategy to assess causal effects on a survival outcome in the presence of continuous time treatment discontinuation, its advantages, and the conclusions one can draw. Our strategy deals with the time-to-event intermediate variable that may not be defined for patients who would not discontinue; moreover, discontinuation time and the primary endpoint are subject to censoring. We employ a flexible model-based Bayesian approach to tackle these complexities, providing easily interpretable results. We apply this Bayesian principal stratification framework to analyze synthetic data of the motivating oncology trial. We simulate data under different assumptions that reflect real scenarios where patients' behavior depends on critical baseline covariates. Supported by a simulation study, we shed light on the role of covariates in this framework: beyond making structural and parametric assumptions more credible, they lead to more precise inference and can be used to characterize patients' discontinuation behavior, which could help inform clinical practice and future protocols.

翻译：在临床试验中，患者可能提前终止治疗，这会破坏初始随机化设计，从而对统计推断提出挑战。药物研发的相关方通常不满足于意向性治疗分析，尽管该分析能提供治疗分配效应的有效因果估计，却无法反映实际接受治疗的效果。本研究受一项肿瘤学随机对照试验的启发：在该试验中，分配至研究性治疗组的患者可能因不良事件而终止治疗。我们提出采用主分层策略，将总体意向性治疗效应分解为按潜在治疗中断行为定义的患者亚群的主因果效应。我们首先阐释如何实施主分层策略来评估存在连续时间治疗中断情况下对生存结局的因果效应，包括其优势与可推导的结论。该策略处理了时间-终点中间变量——对于本不会中断治疗的患者而言，该变量可能无法定义；此外，治疗中断时间与主要终点均存在删失现象。我们采用基于灵活模型的贝叶斯方法应对这些复杂问题，并提供易于解释的结果。我们将此贝叶斯主分层框架应用于分析启发性肿瘤试验的合成数据。通过在不同假设下模拟数据（这些假设反映了患者行为依赖关键基线协变量的真实场景），并结合模拟研究的支持，我们揭示了协变量在此框架中的作用：协变量不仅能增强结构假设与参数假设的可信度，还能提高推断精度，并可用于刻画患者的治疗中断行为特征，从而为临床实践和未来试验方案提供参考依据。