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

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

SUBMITTER: Brantner CL 

PROVIDER: S-EPMC12768387 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature