Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments

TL;DR

This paper introduces an integrative learning framework that combines evidence from multiple RCTs with overlapping treatments to improve individualized treatment rule estimation, especially when individual studies are underpowered.

Contribution

It proposes a novel regularized weighted risk approach that adaptively synthesizes data across studies with different treatment arms for better ITR estimation.

Findings

Integrative methods outperform separate learning in simulations.

The approach improves estimation of value and benefit functions.

Application to depression studies shows enhanced treatment decision-making.

Abstract

An individualized treatment rule (ITR) tailors treatments to a patient's specific characteristics. However, randomized controlled trials (RCTs) are often underpowered to detect the treatment effect heterogeneity needed for reliable ITR estimation. To address this limitation, there is growing interest in leveraging information from multiple studies to improve statistical power and support individualized decision-making. A key challenge in this context is that available RCTs may not evaluate the same set of treatments. In this paper, we propose an integrative learning framework that synthesizes evidence across multiple RCTs that share a common comparator but differ in their alternative treatment arms. Our method integrates information through a regularized weighted misclassification risk function and adaptively determines the contribution of each study to the ITRs of the others. We rigorously study the excess risk of the resulting estimator. Simulation studies demonstrate that the proposed approaches improve the estimation of both value functions and benefit functions. We illustrate the utility of our methodology using data from two landmark studies of major depressive disorder: the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study and the International Study to Predict Optimized Treatment in Depression (iSPOT-D) study, both of which include a selective serotonin reuptake inhibitor as a common treatment arm. We find that the separate learning method outperforms one-size-fits-all methods, and our integrative methods further improve performance.