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Prediction models are used amongst others to inform medical decisions oninterventions. Typically, individuals with high risks of adverse outcomes areadvised to undergo an intervention while those at low risk are advised torefrain from it. Standard prediction models do not always provide risks thatare relevant to inform such decisions: e.g., an individual may be estimated tobe at low risk because similar individuals in the past received an interventionwhich lowered their risk. Therefore, prediction models supporting decisionsshould target risks belonging to defined intervention strategies. Previousworks on prediction under interventions assumed that the prediction model wasused only at one time point to make an intervention decision. In clinicalpractice, intervention decisions are rarely made only once: they might berepeated, deferred and re-evaluated. This requires estimated risks underinterventions that can be reconsidered at several potential decision moments.In the current work, we highlight key considerations for formulating estimandsin sequential prediction under interventions that can inform such interventiondecisions. We illustrate these considerations by giving examples of estimandsfor a case study about choosing between vaginal delivery and cesarean sectionfor women giving birth. Our formalization of prediction tasks in a sequential,causal, and estimand context provides guidance for future studies to ensurethat the right question is answered and appropriate causal estimationapproaches are chosen to develop sequential prediction models that can informintervention decisions.

Risk-based decision making: estimands for sequential prediction under interventions