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，脱落现象普遍存在，高达一半的患者因副作用或其他原因提前退出研究。当脱落具有信息性（即依赖于生存时间）时，会引入删失偏倚，从而导致处理效应估计产生偏差。本文提出了一种假设宽松的框架，用于评估面临删失偏倚时生存分析中条件平均处理效应（CATE）估计的稳健性。与现有研究依赖强假设（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CATE的信息性边界。因此，该框架有助于识别在存在信息性删失情况下治疗仍有效的患者亚组。我们进一步开发了一种新型元学习器，该学习器可利用任意机器学习模型估计边界，并具备良好的理论性质，包括双重稳健性和拟Oracle效率。我们通过数值实验和癌症药物试验应用，展示了该元学习器的实用价值。总体而言，我们的框架为评估存在删失情况下估计处理效应的稳健性提供了实用工具，从而促进在医学和流行病学中可靠利用生存数据生成证据。