Survival analysis deals with modeling the time until an event occurs, and accurate probability estimates are crucial for decision-making, particularly in the competing-risks setting where multiple events are possible. While recent work has addressed calibration in standard survival analysis, the competing-risks setting remains under-explored as it is harder (the calibration applies to both probabilities across classes and time horizon). We show that existing calibration measures are not suited to the competing-risk setting and that recent models do not give well-behaved probabilities. To address this, we introduce a dedicated framework with two novel calibration measures that are minimized for oracle estimators (i.e., both measures are proper). We also introduce some methods to estimate, test, and correct the calibration. Our recalibration methods yield good probabilities while preserving discrimination.

翻译：生存分析旨在对事件发生时间进行建模，其中准确的概率估计对于决策制定至关重要，在存在多种可能事件的竞争风险场景下尤为如此。尽管近期研究已关注标准生存分析中的校准问题，但竞争风险场景下的校准仍未被充分探索，因其难度更高（校准需同时考虑跨类别概率与时间维度）。本文证明现有校准度量方法不适用于竞争风险场景，且近期模型未能产生良好行为的概率估计。为此，我们提出了一个专用框架，包含两种新型校准度量方法，这些方法在理想估计器下可达到最小化（即两种度量均具有适定性）。同时，我们提出了若干估计、检验及修正校准的方法。我们的再校准方法能够在保持判别力的同时生成优质概率估计。