Discrimination and calibration represent two important properties of survival analysis, with the former assessing the model's ability to accurately rank subjects and the latter evaluating the alignment of predicted outcomes with actual events. With their distinct nature, it is hard for survival models to simultaneously optimize both of them especially as many previous results found improving calibration tends to diminish discrimination performance. This paper introduces a novel approach utilizing conformal regression that can improve a model's calibration without degrading discrimination. We provide theoretical guarantees for the above claim, and rigorously validate the efficiency of our approach across 11 real-world datasets, showcasing its practical applicability and robustness in diverse scenarios.

翻译：区分度与校准度是生存分析的两个重要属性，前者评估模型对受试者进行准确排序的能力，后者评估预测结果与实际事件的吻合程度。由于二者性质不同，生存模型难以同时优化两者，尤其是既往许多研究发现提升校准度往往会削弱区分性能。本文提出一种利用保形回归的新方法，能够在保持区分度的前提下提升模型的校准度。我们为上述主张提供了理论保证，并在11个真实世界数据集上严格验证了该方法的有效性，展示了其在多样化场景中的实际适用性与鲁棒性。