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

2026-04-12

AI Summary

This study addresses the challenge that individual randomized controlled trials often lack sufficient sample sizes to reliably estimate individualized treatment rules, while partial overlap in treatment arms across studies hinders effective cross-trial integration. To overcome this, the authors propose an integrative learning framework that combines data from multiple trials sharing a common control group but featuring partially overlapping experimental arms. The method jointly estimates individualized treatment rules by minimizing a regularized, weighted misclassification risk and incorporates an adaptive weighting mechanism to dynamically calibrate each study’s contribution. This approach represents the first adaptive information fusion strategy tailored to settings with partially overlapping treatments. Theoretical analysis establishes bounds on the excess risk of the estimator, and the framework jointly optimizes both value and benefit functions. Simulations and real-world applications (EMBARC and iSPOT-D) demonstrate superior estimation accuracy and clinical utility compared to single-study learning and one-size-fits-all strategies.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Multi-instance/Multi-view Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• individualized treatment rule
• multiple studies
• partially overlapping treatments
• treatment effect heterogeneity
• randomized controlled trials

Innovation

• individualized treatment rule
• integrative learning
• partially overlapping treatments
• regularized weighted misclassification risk
• multi-study synthesis