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

ABSTRACT: 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

SUBMITTER: Shen J 

PROVIDER: S-EPMC12783382 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature