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

Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam, Giovanni Cinà, Ruth Keogh, Jesse Krijthe, Sara Magliacane, Thijs van Ommen, Hein Putter, Maarten van Smeden, Matthew Sperrin, Junfeng Wang, Daniala Weir, Vanessa Didelez, Nan van Geloven

Research output: Working paperPreprintResearch

Abstract

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.
Original languageUndefined/Unknown
Publication statusPublished - 29 Nov 2023

Keywords

  • stat.ME

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