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.

翻译：预测模型被用于指导医疗干预决策。通常，不良结局高风险个体被建议接受干预，而低风险个体则被建议避免干预。标准预测模型并不总能提供与决策相关的风险：例如，某个个体被评估为低风险，可能是因为过去相似个体曾接受过降低风险的干预。因此，支持决策的预测模型应针对属于既定干预策略的风险。过往关于干预下预测的研究假设预测模型仅在单个时间点用于做出干预决策。临床实践中，干预决策很少仅做出一次：它们可能重复、延迟或重新评估。这就要求干预下的估计风险能够在多个潜在决策时刻被重新考量。在当前工作中，我们强调在可指导干预决策的序列预测中制定估计目标的关键考量。我们通过一个关于产妇选择阴道分娩与剖宫产的案例研究，举例说明这些考量。我们在序列、因果和估计目标背景下对预测任务的形式化，为未来研究提供指导，以确保提出正确问题并选择恰当的因果估计方法，从而开发能够指导干预决策的序列预测模型。