发表机构
NHO Nagoya Medical Center; Nagoya University Graduate School of Medicine（日本国立医院机构名古屋医疗中心; 名古屋大学大学院医学研究科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Brier-UDAM框架，将配对预测规则的Brier风险差异分解为平均偏差、离散度和尺度对齐，实现潜在风险解释与推断，并在再入院分析中揭示隐藏抵消。
AI 中文摘要
Brier风险总结了概率预测误差，但并未解释为何两个预测规则存在差异。我们开发了Brier-UDAM，一个用于配对二元预测规则比较的框架，将Brier风险差异分解为平均偏差、离散度和Brier尺度对齐贡献。尽管可观察对比恒等式在代数上遵循Yates型矩分解，该框架赋予这些项潜在风险解释，并表明配对对比可以从观测结果和配对预测中识别，而无需估计条件事件风险函数。对于在同一目标总体中评估的两个规则，精确恒等式ΔR = ΔM + ΔD + ΔL成立，相应的插件估计器在有限样本中精确重现经验配对Brier风险差异。我们推导了基于影响函数的一阶正则对比推断，以及当一阶推断退化时针对平均偏差对比的基于投影的置信程序。模拟证实了可解释的分量行为、目标对比的恢复，以及在正则设置中总体令人满意的推断；投影区间在接近退化时保持覆盖但较为保守。在一项关于30天医院再入院的大型公开数据分析中，具有相似留出Brier风险的模型显示出显著更大且相反的离散度和对齐贡献，揭示了在聚合分数中隐藏的抵消效应。因此，Brier-UDAM提供了配对Brier风险差异的诊断性说明，而校准、区分度和基于信息的评估继续解决其各自的性能问题。
英文摘要
The Brier risk summarizes probability-prediction error but does not explain why two prediction rules differ. We develop Brier-UDAM, a framework for paired binary prediction-rule comparisons that separates the Brier-risk difference into mean-bias, dispersion, and Brier-scale alignment contributions. Although the observable contrast identity follows algebraically from Yates-type moment decompositions, the framework gives these terms a latent-risk interpretation and shows that paired contrasts can be identified from observed outcomes and paired predictions without estimating the conditional event-risk function. For two rules evaluated in the same target population, the exact identity $ΔR = ΔM + ΔD + ΔL$ holds, and the corresponding plug-in estimators reproduce the empirical paired Brier-risk difference exactly in finite samples. We derive influence-function-based inference for first-order regular contrasts and a projection-based confidence procedure for the mean-bias contrast when first-order inference degenerates. Simulations confirmed interpretable component behavior, recovery of the target contrasts, and generally satisfactory inference in regular settings; projection intervals retained coverage near degeneracy but were conservative. In a large public-data analysis of 30-day hospital readmission, models with similar held-out Brier risks showed substantially larger and opposing dispersion and alignment contributions, revealing cancellation that was hidden in the aggregate score. Brier-UDAM therefore provides a diagnostic account of paired Brier-risk differences, while calibration, discrimination, and information-based assessments continue to address their distinct performance questions.
Comments78 pages, 6 figures, 8 tables. Includes supplementary material
