An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.

ABSTRACT: Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our

SUBMITTER: Maleyeff L 

PROVIDER: S-EPMC11639530 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature