1 research outputs found
Risk-based decision making: estimands for sequential prediction under interventions
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.Comment: 32 pages, 2 figure