Risk-based decision making: estimands for sequential prediction under interventions
基于风险的决策：干预下序贯预测的估计目标AI 解读 ↓
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基于风险的决策：干预下序贯预测的估计目标
- 研究问题
- 当干预决策会被重复、推迟或重新评估时，风险预测应针对哪些明确的干预策略，才能避免既往治疗造成的风险解释混淆？
- 方法与思路
- 作者在序贯、因果和估计目标框架下讨论预测任务的定义，并通过产妇选择阴道分娩或剖宫产的案例展示不同目标的设置。
- 材料陈述的贡献
- 摘要称，研究明确了序贯干预预测中定义估计目标的关键考虑，为未来选择合适的因果估计方法和决策相关风险提供指导。
- 阅读关注点
- 应核对决策时点、可用历史信息、未来干预策略及预测结局如何共同定义目标，以及不同目标对识别和估计方法的要求。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要主要提供目标定义指导，未说明具体识别条件、估计性能或临床决策收益。
原文摘要
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions.