Designing a quasi-experiment to study the clinical impact of adaptive risk prediction models
评估自适应风险预测模型临床影响的准实验设计AI 解读 ↓
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评估自适应风险预测模型临床影响的准实验设计
- 研究问题
- 当临床风险模型与干预阈值持续更新时，如何设计回归不连续分析，使模型和决策规则的适应变化不破坏局部效应识别？
- 方法与思路
- 作者刻画更新引入的干扰，用假想患者排序下的反事实风险恢复局部可交换性，并利用风险向量的低维结构构造估计器。
- 材料陈述的贡献
- 摘要称，框架给出识别条件与估计策略，在心血管预防模拟中，即使阈值和预测模型持续调整，也能准确恢复处理效应。
- 阅读关注点
- 应核对假想排序的定义和可计算性、适应性干扰结构、局部可交换性条件，以及风险向量低维性如何支持估计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出识别条件，效果证据来自模拟，不能据此确认真实临床有效性。
原文摘要
Clinical risk prediction is a valuable tool for guiding healthcare interventions toward those most likely to benefit. Yet, evaluating the pairing of a risk prediction model with an intervention using randomized controlled trials presents substantial challenges, making quasi-experimental designs an attractive alternatives. Existing designs, however, assume that both the model and the decision rules used to trigger interventions (typically a risk threshold) remain fixed. This limits their utility in modern healthcare, where both are routinely updated. We introduce a regression discontinuity framework that accommodates adaptation in both the model and the risk threshold. We precisely characterize the form of interference introduced by these adaptations and exploit this structure to establish conditions for identification and thus design estimation strategies. The key idea is to define counterfactual risks-the scores patients would have received under hypothetical reorderings-thereby restoring local exchangeability and enabling valid estimation of the local average treatment effect. Our estimator leverages the fact that, although counterfactual risk vectors grow with time, they typically lie in a low-dimensional space. In simulations of cardiovascular prevention programs, we show that our method accurately recovers treatment effects even as thresholds adapt to meet operational or clinical targets and models are updated to align predicted and observed outcomes or to exclude demographic predictors such as race.