Two-stage Bayesian network meta-analysis of individualized treatment rules for multiple treatments with siloed data

Individualized treatment rules leverage patient-level information to tailor treatments for individuals. Estimating these rules, with the goal of optimizing expected patient outcomes, typically relies on individual-level data to identify the variability in treatment effects across patient subgroups defined by different covariate combinations. To increase the statistical power for detecting treatment-covariate interactions and the generalizability of the findings, data from multisite studies are often used. However, sharing sensitive patient-level health data is sometimes restricted. Additionally, due to funding or time constraints, only a subset of available treatments can be included at each site, but an individualized treatment rule considering all treatments is desired. In this work, we adopt a two-stage Bayesian network meta-analysis approach to estimate individualized treatment rules for multiple treatments using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters that fully characterize the optimal individualized treatment rule. We illustrate the method's application through an analysis of data from the Sequenced Treatment Alternatives to Relieve Depression study, the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care study, and the Research Evaluating the Value of Augmenting Medication with Psychotherapy study.