针对右删失数据，提出一种双重稳健的生存分析预测方法。
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
- 用机器学习模型填补缺失删失时间，再通过加权推断校准生存模型
- 在模拟与真实数据上表现良好，尤其在生存模型不准时仍保持稳定
- 适合临床研究中需可靠生存时间下界预测的场景
我们提出一种基于约束推断的生存分析方法，用于从右删失数据中构建生存时间的下界预测。该方法通过机器学习模型对未观测到的删失时间进行插补，并利用加权约束推断校准生存模型。理论分析表明该方法具有渐近双重稳健性。在模拟和真实数据上的实验显示，该方法能提供相对信息量丰富的预测推断，在生存模型可能不准确的挑战性场景中尤为稳健。
原文摘要 · Abstract (English)
We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model calibrated via weighted conformal inference. This approach is theoretically supported by an asymptotic double robustness property. Empirical studies on simulated and real data demonstrate that our method leads to relatively informative predictive inferences and is especially robust in challenging settings where the survival model may be inaccurate.
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