提出新校准框架，让竞争风险生存模型的概率更准确。
On the calibration of survival models with competing risks
- 设计专用校准指标，解决竞争风险场景下的概率偏差问题。
- 实验证明现有模型概率不理想，新方法显著改善校准度。
- 适合医疗决策等依赖精确概率的高风险场景使用。
生存分析旨在建模事件发生的时间，准确的概率估计对决策至关重要，尤其在存在多种可能事件的竞争风险设置中。尽管近期研究关注标准生存分析的校准问题，竞争风险场景仍缺乏足够探索，因其校准需同时满足各类别概率与时间跨度的一致性要求。本文指出现有校准度量不适用于竞争风险场景，且当前模型给出的概率表现不佳。为此，我们提出一个专用框架，包含两个新型校准度量，其在理想估计器下可被最小化（即两者均为合理度量）。此外，我们还引入了估计、检验与修正校准的方法。实验表明，所提重校准方法在保持判别能力的同时显著提升概率准确性。
原文摘要 · Abstract (English)
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.
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