Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons. When dropout is informative (i.e., dependent on survival time), it introduces censoring bias, because of which treatment effect estimates are also biased. In this paper, we propose an assumption-lean framework to assess the robustness of conditional average treatment effect (CATE) estimates in survival analysis when facing censoring bias. Unlike existing works that rely on strong assumptions, such as non-informative censoring, to obtain point estimation, we use partial identification to derive informative bounds on the CATE. Thereby, our framework helps to identify patient subgroups where treatment is effective despite informative censoring. We further develop a novel meta-learner that estimates the bounds using arbitrary machine learning models and with favorable theoretical properties, including double robustness and quasi-oracle efficiency. We demonstrate the practical value of our meta-learner through numerical experiments and in an application to a cancer drug trial. Together, our framework offers a practical tool for assessing the robustness of estimated treatment effects in the presence of censoring and thus promotes the reliable use of survival data for evidence generation in medicine and epidemiology.

翻译：在临床研究中，脱落现象十分常见，高达半数的患者可能因副作用或其他原因提前退出研究。当脱落具有信息性（即与生存时间相关）时，会引入删失偏倚，从而导致治疗效果估计也产生偏倚。本文提出了一种假设宽松的框架，用于评估存在删失偏倚时生存分析中条件平均治疗效应估计的稳健性。与现有工作依赖强假设（如非信息性删失）以获取点估计不同，我们采用部分识别方法推导出CATE的信息性边界。因此，我们的框架有助于识别即使在存在信息性删失的情况下治疗仍然有效的患者亚组。我们进一步开发了一种新颖的元学习器，该学习器能够利用任意机器学习模型估计这些边界，并具备良好的理论性质，包括双重稳健性和拟Oracle效率。我们通过数值实验以及在癌症药物试验中的应用，展示了该元学习器的实用价值。总之，我们的框架为评估存在删失时估计治疗效应的稳健性提供了一个实用工具，从而促进了生存数据在医学和流行病学中用于证据生成的可靠使用。