标题： 设计一个准实验以研究自适应风险预测模型的临床影响 显示英文标题

标题： Designing a quasi-experiment to study the clinical impact of adaptive risk prediction models

摘要： 临床风险预测是指导医疗干预措施向最可能受益的人群提供的重要工具。 然而，使用随机对照试验评估风险预测模型与干预措施的配对存在重大挑战，使得准实验设计成为有吸引力的替代方案。 然而，现有的设计假设模型和用于触发干预的决策规则（通常是风险阈值）保持不变。 这限制了它们在现代医疗保健中的实用性，因为在现代医疗保健中，这两者都会经常更新。 我们引入了一个回归不连续框架，可以适应模型和风险阈值的变化。 我们精确地描述了这些适应所带来的干扰形式，并利用这种结构来建立识别条件，从而设计估计策略。 关键思想是定义反事实风险——患者在假设重新排序下应获得的分数——从而恢复局部可交换性，并能够有效估计局部平均处理效应。 我们的估计器利用了这样一个事实：尽管反事实风险向量随时间增长，但它们通常位于低维空间中。 在心血管预防项目的模拟中，我们表明，即使阈值适应以满足操作或临床目标，模型也更新以使预测结果与观察结果一致，或排除种族等人口统计学预测因子，我们的方法也能准确恢复处理效应。 显示英文摘要

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.