BAP: Bayesian Biomarker-Assisted Platform Design for Dose Ranging in Multi-Agent Multi-Dose Trials

Assessing the long-term benefits of new treatments can be expensive and time-consuming, particularly in disease areas with unmet medical needs. While platform trials enable the evaluation of multiple interventions simultaneously, they currently cannot assess studies involving multiple agents and doses or utilize longitudinal biomarkers in decision-making. We propose a Bayesian biomarker-assisted platform design that offers a unified framework for evaluating multiple investigational agents and their doses in a multi-stage, randomized controlled trial. The design streamlines the drug evaluation process and decreases development costs by including proof-of-concept, futility and superiority monitoring, and dose optimization in a single trial, while avoiding over-allocating patients to a shared placebo or active control arm. To facilitate making real-time interim group sequential decisions, temporarily unobserved long-term responses are estimated from longitudinal biomarker measurements. Design parameters and the maximum sample size are fine-tuned to achieve good frequentist properties. The proposed design is illustrated by a trial of three targeted agents for systemic lupus erythematosus, evaluated by their 24-week response rates. Extensive simulations show that the proposed design compares favorably to several conventional platform designs.