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

TL;DR

This paper discusses how to define and estimate risks in sequential decision-making for interventions, emphasizing the importance of targeted estimands that reflect real-world clinical decision processes.

Contribution

It introduces a formal framework for defining estimands in sequential prediction under interventions, addressing the gap in existing models that assume single-time decisions.

Findings

Highlights key considerations for estimand formulation in sequential interventions.

Provides examples of estimands in a case study on childbirth interventions.

Guides future research on causal estimation for sequential decision-making.

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.