DOD-BART: machine learning-based dose optimization design incorporating patient-level prognostic factors via Bayesian additive regression trees.

Dose optimization is a critical stage of drug development in oncology and other disease areas. Early phase clinical trials are inherently heterogeneous due to their exploratory nature. The process of identifying an optimal dose involves careful considerations of the patient population, evaluation of therapeutic potential, and exploration of the dose-response and dose-toxicity relationships to ensure that it is safe and effective for the intended use. However, the complex mechanism of actions and uncertainties during dose optimization often introduce substantial gaps between those early phase trials and phase 3 randomized control trials. These gaps can indeed increase the chances of failure. To address these challenges, we propose a novel seamless phase I/II design, namely DOD-BART design, which utilizes machine learning technique, specifically Bayesian Additive Regression Trees (BART) to fully incorporate patient-level prognostic factors and outcomes. Our design provides a streamlined approach for dose exploration and optimization, automatically updated with emerging data to allocate patients to the most promising dose levels. DOD-BART elucidates disease relationships, analyzes and synthesizes emerging data, augments operational efficiency, and guides dose optimization for suitable population. Simulation studies demonstrate the robust performances of the DOD-BART design across a variety of realistic settings, with high probabilities of correctly identifying the optimal dose, allocating patients more to tolerable and efficacious dose levels, making less biased estimates, and efficiently utilizing patients' data.