Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings.

翻译：生存预测通常涉及从删失数据集中估计事件发生时间分布。先前的研究方法主要集中于提升区分度和边际校准。本文重点强调了条件校准在实际应用中的重要性——特别是其在个体决策中的作用。我们提出了一种基于共形预测的方法，该方法利用模型在观测时间点预测的个体生存概率。此方法有效提升了模型的边际校准与条件校准性能，同时不损害其区分能力。我们为边际校准和条件校准提供了渐近理论保证，并在15个不同的真实世界数据集上进行了广泛测试，证明了该方法在不同场景下的实际有效性和普适性。