群体层面的决策曲线分析在亚组效用异质性下可能误导预测模型有用性的评估
Population-Level Decision Curve Analysis May Mislead the Evaluation of Prediction Model Usefulness under Subgroup Utility Heterogeneity
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中文总结 AI 辅助
本研究揭示当亚组间效用异质性存在时，群体层面决策曲线分析可能误导预测模型评估，提出不一致区域与稳健性框架以识别和评估该风险。
中文摘要 AI 辅助
背景：决策曲线分析（DCA）使用净收益（NB）评估预测模型的有用性。群体层面的净收益常被解释为群体层面预期效用的代理指标，但这假设了各亚组之间的效用具有可比性。我们旨在刻画亚组效用异质性何时可能使群体层面的DCA结论失效。方法：我们在包含具有不同效用值的亚组的人群中，将预测驱动的治疗与默认策略进行比较。我们推导出一个不一致区域：即亚组特定的ΔNB值的组合，在该区域内群体层面的净收益和效用会倾向于不同的策略。我们还开发了一个实用的稳健性框架。结果：亚组特定ΔNB值的相反符号为可能的不一致性提供了警示信号。当亚组规模更相似且亚组特定的a-c值差异更大时，不一致区域更大，其中a-c是真阳性相对于假阴性的增量效用。当a-c在各亚组间存在差异时，群体层面的净收益在不同隐含效用尺度上合并了数量，并可能与群体效用冲突。一项真实世界案例研究说明了这一问题。结论：将群体层面的净收益作为群体效用的代理指标隐含地假设了各亚组间a-c的同质性。亚组DCA和我们的框架可以识别并评估效用异质性何时可能使群体层面的结论失效。
英文摘要
Background Decision curve analysis (DCA) evaluates prediction model usefulness using net benefit (NB). Population-level NB is often interpreted as a proxy for population-level expected utility, but this assumes comparability of utility across subgroups. We aimed to characterize when subgroup utility heterogeneity may invalidate population-level DCA conclusions. Methods We compared prediction-driven treatment with default strategies in populations containing subgroups with different utility values. We derived an inconsistency region: combinations of subgroup-specific ΔNB values for which population-level NB and utility favor different strategies. We also developed a practical robustness framework. Results Opposite signs of subgroup-specific ΔNB provide a warning signal for possible inconsistency. The inconsistency region is larger when subgroup sizes are more similar and subgroup-specific \(a-c\) values are more different, where \(a-c\) is the incremental utility of a true positive relative to a false negative. When \(a-c\) differs across subgroups, population-level NB combines quantities on different implicit utility scales and may conflict with population utility. A real-world case study illustrates the problem. Conclusions Using population-level NB as a proxy for population utility implicitly assumes homogeneous \(a-c\) across subgroups. Subgroup DCA and our framework can identify and assess when utility heterogeneity may invalidate population-level conclusions.
发表机构
- Maastricht University（马斯特里赫特大学）
- KU Leuven（荷语鲁汶大学）
- Leiden University Medical Center（莱顿大学医学中心）
- University Medical Center Utrecht, Utrecht University（乌得勒支大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。
