In controlled trials, “treatment switching” occurs when patients in one treatment group switch to alternative treatments during the trial, and poses challenges to treatment effect evaluation owing to crossover of the treatments groups. In this work, we assume that treatment switching can occur after some disease progression event and view the progression and death events as two semicompeting risks. The proposed model consists of a copula model for the joint distribution of time‐to‐progression (TTP) and overall survival (OS) up to the earlier of the two events, as well as a conditional hazard model for OS subsequent to progression. The copula model facilitates assessing the marginal distributions of TTP and OS separately from the association between the two events, and, in particular, the treatment effect on OS in the absence of treatment switching. The proposed conditional hazard model for death subsequent to progression allows us to assess the treatment switching (crossover) effect on OS given occurrence of progression and covariates. Semiparametric proportional hazards models are employed in the marginal models for TTP and OS. A nonparametric maximum likelihood procedure is developed for model inference, which is verified through asymptotic theory and simulation studies. The proposed analysis is applied to a lung cancer dataset to illustrate its real utility.

中文翻译：

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基于半参数copula的分析，用于在存在治疗切换的情况下的治疗效果。

在对照试验中，当一个治疗组中的患者在试验期间转换为替代治疗时，就会发生“治疗转换”，由于治疗组的交叉，给治疗效果评估带来了挑战。在这项工作中，我们假设在某些疾病进展事件之后可能发生治疗切换，并将进展和死亡事件视为两个半竞争风险。所提出的模型包括用于直到两个事件中的较早事件的进展时间（TTP）和总体生存期（OS）的联合分布的copula模型，以及进展后OS的条件危害模型。copula模型有助于独立于两个事件之间的关联来评估TTP和OS的边缘分布，尤其是在不进行治疗转换的情况下对OS的治疗效果。对于进展后死亡的拟议条件性危害模型使我们能够在发生进展和协变量的情况下评估对OS的治疗转换（交叉）效应。TTP和OS的边际模型采用半参数比例风险模型。开发了一种用于模型推理的非参数最大似然程序，并通过渐近理论和仿真研究对其进行了验证。拟议的分析应用于肺癌数据集，以说明其实际用途。开发了用于模型推理的非参数最大似然程序，并通过渐近理论和仿真研究对其进行了验证。拟议的分析应用于肺癌数据集，以说明其实际用途。开发了用于模型推理的非参数最大似然程序，并通过渐近理论和仿真研究对其进行了验证。拟议的分析应用于肺癌数据集，以说明其实际用途。