提出新方法评估生存分析中治疗效果的稳健性，应对患者提前退出带来的偏差。
Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
- 用部分识别法构建治疗效果的可信区间，不依赖强假设。
- 在模拟和真实数据中验证了方法能准确识别有效治疗人群。
- 适用于各类机器学习模型，对数据偏差有强鲁棒性。
临床研究中患者中途退出常见，高达一半患者因副作用等原因提前离开。当退出与生存时间相关时，会引入删失偏差，导致治疗效果估计偏误。本文提出一种无需强假设的框架，用于评估生存分析中条件平均治疗效应（CATE）的稳健性。不同于传统依赖非信息删失假设的方法，我们采用部分识别技术推导出CATE的有界估计。该框架可帮助识别在信息性删失下仍有效的患者亚群。进一步提出一种新型模型无关元学习器SurvB-learner，能结合任意机器学习模型使用，并具备双重稳健性和准最优效率等理论优势。实验在模拟和真实数据上均验证了该方法的有效性。
原文摘要 · Abstract (English)
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 propose a novel model-agnostic meta-learner, called SurvB-learner, to estimate the bounds that can be used in combination with arbitrary machine-learning models, and that has favorable theoretical properties such as double-robustness and quasi-oracle efficiency. We finally demonstrate the effectiveness of our meta-learner across various experiments using both simulated and real-world data.
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