Doubly-Robust Bayesian Estimation of Optimal Individualized Treatment Rules using Network Meta-Analysis

2026-02-03

AI Summary

This study addresses key challenges in estimating individualized treatment rules (ITRs) for major depressive disorder (MDD)—including low statistical power, absence of head-to-head trials, and missing outcome data—by proposing a two-stage Bayesian framework grounded in network meta-analysis (NMA). In the first stage, ITRs are estimated within each study; in the second, results are integrated via an enhanced Bayesian NMA that incorporates a doubly robust mechanism to handle outcomes missing at random. The method innovatively combines Bayesian bootstrap with dynamic weighted least squares (BBdWOLS) and fully accounts for the study-specific variance–covariance structure of ITR estimates, substantially improving robustness and uncertainty quantification. Simulations and analyses of real-world MDD pharmacotherapy data demonstrate that the proposed approach outperforms existing methods in both efficiency and robustness, offering strong support for personalized treatment decision-making.

Technology Category

• Machine Learning: Bayesian Learning
• Intelligent Robots: State Estimation
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

An optimal individualized treatment rule (ITR) is a function that takes a patient's characteristics, such as demographics, biomarkers, and treatment history, and outputs a treatment that is expected to give the best outcome for that patient. Major Depressive Disorder (MDD) is a common and disabling mental health condition for which an optimal ITR is of interest. Unfortunately, the power to detect treatment-covariate interactions in individual studies of MDD treatments is low. Additionally, all treatments of interest are not compared head-to-head in a single study. Network meta-analysis (NMA) is a method of synthesizing data from multiple studies to estimate the relative effects of a set of treatments. Recently, two-stage ITR NMA was proposed as a method to estimate ITRs that has the potential to improve power and simultaneously consider all relevant treatment options. In the first stage, study-specific ITRs are estimated, and in the second stage, they are pooled using a Bayesian NMA model. The existing approach is vulnerable to model misspecification and fails to address missing outcomes, which occur in the MDD data. We overcome these challenges by proposing Bayesian Bootstrap dynamic Weighted Ordinary Least Squares (BBdWOLS), a doubly-robust approach to ITR estimation that accounts for missing at random outcomes and naturally quantifies the uncertainty in estimation. We also propose an improvement to the NMA model that incorporates the full variance-covariance matrix of study-specific estimates. In a simulation study, we show that our fully Bayesian ITR NMA method is more robust and efficient than the existing approach. We apply our method to the motivating dataset consisting of three studies of pharmacological treatments for MDD, and explore how ITR NMA results can support personalized decision making in this context.

Problem

• individualized treatment rule
• Major Depressive Disorder
• network meta-analysis
• missing outcomes
• treatment-covariate interaction

Innovation

• doubly-robust
• Bayesian bootstrap
• network meta-analysis
• individualized treatment rules
• missing outcomes