A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions
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病例构成变化下预测性能的因果视角：预后与诊断的区分度和校准度
- 研究问题
- 当临床应用中的患者构成发生变化时，预测任务的因果方向如何影响区分度与校准度，以及模型在不同场景间的表现？
- 方法与思路
- 作者提出区分因果方向和反因果方向预测的病例构成框架，并通过模拟与心血管疾病预测模型的实证验证考察其含义。
- 材料陈述的贡献
- 摘要称顺因果方向预测的校准度稳定而区分度变化，反因果方向则相反，借此解释预后与诊断模型的不同迁移表现。
- 阅读关注点
- 应核对病例构成变化的精确定义、被保持不变的机制和性能指标，以及上述稳定性结论对模型正确设定的依赖。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明稳定性结论的完整条件，不能推广至任意分布变化。
原文摘要
Prediction models need reliable predictive performance as they inform clinical decisions, aiding in diagnosis, prognosis, and treatment planning. The predictive performance of these models is typically assessed through discrimination and calibration. Changes in the distribution of the data impact model performance and there may be important changes between a model's current application and when and where its performance was last evaluated. In health-care, a typical change is a shift in case-mix. For example, for cardiovascular risk management, a general practitioner sees a different mix of patients than a specialist in a tertiary hospital. This work introduces a novel framework that differentiates the effects of case-mix shifts on discrimination and calibration based on the causal direction of the prediction task. When prediction is in the causal direction (often the case for prognosis predictions), calibration remains stable under case-mix shifts, while discrimination does not. Conversely, when predicting in the anti-causal direction (often with diagnosis predictions), discrimination remains stable, but calibration does not. A simulation study and empirical validation using cardiovascular disease prediction models demonstrate the implications of this framework. The causal case-mix framework provides insights for developing, evaluating and deploying prediction models across different clinical settings, emphasizing the importance of understanding the causal structure of the prediction task.