RABOTS: A Randomized Adaptive Bayesian Optimization Two-Stage Seamless Design for Dose Optimization and...

Dose optimization (DO) is a significant paradigm in clinical trials, with the goal of identifying an optimal dose that preserves maximal efficacy while minimizing toxicity. In this paper, we propose a Randomized Adaptive Bayesian Optimization Two-stage Seamless (RABOTS) design that integrates DO with proof-of-concept (PoC) evaluation. In Stage I, an adaptive dose selection process is implemented to drop sub-optimal dose levels based on a pre-specified decision algorithm. In Stage II, a Bayesian Go/No-Go criterion is applied to the selected dose, and a dynamic borrowing approach is recommended to enhance statistical efficiency by leveraging information from the dropped dose. We evaluate the performance of RABOTS by applying different information borrowing methods across multiple dose levels through a range of simulation scenarios. Results show that the RABOTS dose selection stage reliably identifies the dose with superior efficacy and favors the lower dose when both doses exhibit similar efficacy, thereby emphasizing safety. In the PoC stage, the SAM prior borrowing method adaptively addresses prior-data conflict, effectively balancing Bayesian power and Bayesian type I error rate. We further examine how the RABOTS key tuning parameters, including the clinically meaningful effect threshold ( $c_{\theta }$ ) and Stage I sample size ( $N_1$ ), influence its operating characteristics. The RABOTS offers a flexible and efficient framework for dose optimization and early efficacy assessment in oncology drug development. Future extensions may include more complex dose structures, the incorporation of safety and alternative endpoint types.