An Adaptive Enrichment Design Using Bayesian Model Averaging for Selection and Threshold-Identification of Tailoring Variables

Music City Center 

Precision medicine is transforming healthcare by personalizing treatments, improving outcomes, and reducing costs. Clinical trials increasingly target patient subgroups with better treatment responses. Biomarker-driven adaptive enrichment designs, which start with a general population and later focus on treatment-sensitive individuals, are gaining popularity. Inspired by a study on positive airway pressure for sleep apnea and cardiovascular outcomes, we propose a Bayesian adaptive enrichment design. It dynamically identifies key biomarkers using free knot B-splines and Bayesian model averaging. Interim analyses assess biomarker-defined subgroups, allowing early trial termination for efficacy or futility and restricting enrollment to treatment-sensitive patients. We address pre-categorized and continuous biomarkers with complex, nonlinear relationships and compare our design to a standard fixed-cutoff approach through simulations.