The estimand framework provides guidance on handling intercurrent events, such as treatment discontinuation, in the analysis of clinical trial responses. Under ICH E9(R1), the treatment policy (TP) strategy incorporates post-discontinuation data to reflect treatment effects in real-world practice. However, many existing approaches focus primarily on imputing missing endpoint values for lost-to-follow-up subjects and do not explicitly model completers, retrieved dropouts (RDs), and lost-to-follow-up subjects within a unified framework. This may obscure the relationship between modeling assumptions and the estimand of interest when RD data are present. We propose a likelihood-based model for continuous endpoints that integrates data from all subject categories, including RDs. The approach combines an analysis of covariance formulation with a probit model for treatment discontinuation, enabling explicit formulation of treatment effects for estimands defined using the hypothetical and TP strategies. Estimation is carried out via a computationally efficient maximum likelihood procedure. Numerical studies demonstrate that the proposed method achieves improved bias and variability properties compared with commonly used imputation-based approaches.

翻译：估计量框架为临床试验结局分析中处理治疗中断等并发事件提供了指导。根据ICH E9(R1)指南，治疗策略（TP）通过纳入中断后的数据来反映真实临床实践中的治疗效果。然而，现有方法多聚焦于对失访受试者缺失终点值的插补，未能在统一框架下显式建模完成者、检索退出者（RDs）和失访受试者。这可能导致当存在RD数据时，建模假设与目标估计量之间的关系变得模糊。我们提出一种基于似然的连续终点模型，整合了包括RD在内的所有受试者类别数据。该方法将协方差分析公式与治疗中断的probit模型相结合，能够显式定义基于假设策略和TP策略的估计量所对应的治疗效果。参数估计采用计算高效的最大似然程序实现。数值研究表明，与常用插补方法相比，本方法在偏差和变异特性方面均有改进。