Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration

2025-09-04

AI Summary

Personalized treatment for depression faces challenges in modeling treatment effect heterogeneity and integrating evidence across randomized controlled trials (RCTs). This study proposes a two-stage meta-analytic framework: in Stage I, multiple causal models—including causal forests, Bayesian additive regression trees (BART), and parametric regression—are fitted within each RCT to estimate conditional average treatment effects (CATEs); in Stage II, a hierarchical meta-analysis integrates CATE estimates across trials, jointly modeling within- and between-study heterogeneity to produce statistically valid CATE prediction intervals. The framework enhances external validity and uncertainty quantification, accommodates multi-model inputs, and supports clinical decision-making. Empirical analysis of duloxetine versus vortioxetine reveals no significant treatment-effect heterogeneity, with age emerging as a potential effect modifier; the resulting prediction intervals more comprehensively capture true uncertainty than conventional confidence intervals.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To advance individualized treatment allocation, integrating data across multiple randomized controlled trials (RCTs) can enhance our understanding of treatment effect heterogeneity by increasing available information. However, extending these inferences to individuals outside of the original RCTs remains crucial for clinical decision-making. We introduce a two-stage meta-analytic method that predicts conditional average treatment effects (CATEs) in target patient populations by leveraging the distribution of CATEs across RCTs. Our approach generates 95% prediction intervals for CATEs in target settings using first-stage models that can incorporate parametric regression or non-parametric methods such as causal forests or Bayesian additive regression trees (BART). We validate our method through simulation studies and operationalize it to integrate multiple RCTs comparing depression treatments, duloxetine and vortioxetine, to generate prediction intervals for target patient profiles. Our analysis reveals no strong evidence of effect heterogeneity across trials, with the exception of potential age-related variability. Importantly, we show that CATE prediction intervals capture broader uncertainty than study-specific confidence intervals when warranted, reflecting both within-study and between-study variability.

Problem

• Predicting optimal depression treatment for individual patients
• Integrating data from multiple randomized controlled trials
• Extending treatment effect inferences to external populations

Innovation

• Two-stage meta-analytic method for predicting CATEs
• Leverages RCT data distribution for treatment effect heterogeneity
• Generates prediction intervals using parametric and non-parametric models