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特性，前者衡量模型准确排序个体的能力，后者评估预测结果与实际事件的一致性。由于两者性质迥异，生存模型难以同时优化二者，尤其许多先前研究发现，提升校准度往往会导致区分度下降。本文提出一种基于保形回归的新方法，可在不降低区分度的前提下提升模型校准度。我们为上述论断提供了理论保证，并在11个真实数据集上严格验证了该方法的有效性，展示了其在不同场景下的实际应用价值与鲁棒性。