Selecting optimal subgroups for treatment using many covariates

TL;DR

This paper develops a unified framework for selecting optimal subgroups for treatment based on covariates, applicable across various resource and cost scenarios, and proposes new trial designs for implementation.

Contribution

It introduces a general optimal treatment selection rule applicable to multiple settings and proposes innovative trial designs for practical application in healthcare.

Findings

Optimal treatment rule involves treating individuals with a predicted mean difference exceeding a threshold.

The threshold varies depending on resource constraints, costs, and side effects.

Proposes new randomized trial designs to implement these optimal rules.

Abstract

We consider the problem of selecting the optimal subgroup to treat when data on covariates is available from a randomized trial or observational study. We distinguish between four different settings including (i) treatment selection when resources are constrained, (ii) treatment selection when resources are not constrained, (iii) treatment selection in the presence of side effects and costs, and (iv) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios but the form of the optimal treatment selection rule does not. The results suggest a move away from traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in health care practice.