Intervention effects based on potential benefit

TL;DR

This paper introduces the conditional potential benefit (CPB) metric to optimize individualized treatment policies, especially under resource constraints, by measuring expected improvements from tailored interventions.

Contribution

It proposes a new CPB metric for assessing treatment effects, derives optimal treatment rules under constraints, and provides robust estimators with application to ICU transfer data.

Findings

CPB effectively identifies subgroups with high treatment benefit.

Targeting high-CPB individuals maximizes outcomes under limited intervention capacity.

Method demonstrates practical utility in healthcare decision-making.

Abstract

Optimal treatment rules are mappings from individual patient characteristics to tailored treatment assignments that maximize mean outcomes. In this work, we introduce a conditional potential benefit (CPB) metric that measures the expected improvement under an optimally chosen treatment compared to the status quo, within covariate strata. The potential benefit combines (i) the magnitude of the treatment effect, and (ii) the propensity for subjects to naturally select a suboptimal treatment. As a consequence, heterogeneity in the CPB can provide key insights into the mechanism by which a treatment acts and/or highlight potential barriers to treatment access or adverse effects. Moreover, we demonstrate that CPB is the natural prioritization score for individualized treatment policies when intervention capacity is constrained. That is, in the resource-limited setting where treatment options are freely accessible, but the ability to intervene on a portion of the target population is constrained (e.g., if the population is large, and follow-up and encouragement of treatment uptake is labor-intensive), targeting subjects with highest CPB maximizes the mean outcome. Focusing on this resource-limited setting, we derive formulas for optimal constrained treatment rules, and for any given budget, quantify the loss compared to the optimal unconstrained rule. We describe sufficient identification assumptions, and propose nonparametric, robust, and efficient estimators of the proposed quantities emerging from our framework. Finally, we illustrate our methodology using data from a prospective cohort study in which we assess the impact of intensive care unit transfer on mortality.