提出新评估方法，解决生存模型中删失依赖问题。
Overcoming Dependent Censoring in the Evaluation of Survival Models
- 用阿基米德柯西函数建模事件与删失关联，改进传统评分
- 在12个数据集上比IPCW平均降低12%-16%误差
- 适合处理删失与事件相关的医学生存分析任务
当事件时间与删失时间在给定可观测协变量下不独立时，即发生依赖性删失，这使生存模型评估复杂化。常用指标如Brier得分通常采用逆概率删失加权（IPCW）处理右删失，但IPCW仅在删失分布与事件时间条件独立时有效。本文提出基于阿基米德柯西函数和柯西-图形估计器的依赖性Brier得分，并证明其边际时间估计量的一致性和渐近正态性。为评估该指标，引入半合成框架，在保持原始协变量结构和已知事件时间的同时生成真实依赖性删失。在12个数据集上的实验表明，所提方法相比IPCW平均降低12%-16%的估计误差。源代码见https://github.com/thecml/DependentEVAL。
原文摘要 · Abstract (English)
Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This complicates survival model evaluation because widely used metrics, such as the Brier score, typically handle right-censoring using inverse probability of censoring weighting (IPCW). Unfortunately, IPCW is valid only when the estimated censoring distribution is independent of the event time. We propose a dependent Brier score based on an Archimedean copula and the Copula-Graphic estimator, and establish consistency and asymptotic normality of its margin-time estimator. To evaluate the metric, we introduce a semi-synthetic framework that creates realistic dependent censoring while preserving the original covariate structure and known event times. Across 12 datasets, the proposed metric reduces estimation error by 12-16\% on average relative to IPCW. Source code is available at https://github.com/thecml/DependentEVAL.
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