P09 Bayesian Simulation-Guided Designs for Adaptive Clinical Trials: Potential Synergies of Open Source Code and Statistical Software Tools.

Multi-arm multi-stage (MAMS) trials represent an efficient approach in clinical trial design. This design type allows for testing of multiple treatment arms simultaneously within one protocol, assigning patients to the most promising arms in an adaptive manner, all while controlling for type-1 error. Key to this approach is the choice multiplicity comparison procedures (MCPs), and choice of treatment selection rules. In this case study, we focused on assessing multiple treatment selection rules, including posterior probabilities and Bayesian approaches, using custom R coding integrated in commercial statistical software to optimize a MAMS study design. Leveraging the computing capabilities of commercial software, alongside the flexibility of R coding allowed us to assess a variety of treatment selection rules efficiently and comprehensively. Software-native selection algorithms furthered our optimization aims by offering optimized design candidates for comparison. Our simulation-based approach enhanced probability of success by comparing, side-by-side, different novel treatment selection rules, and choosing the best fit rule for the study at hand. We believe that combining custom code alongside statistical software offers a comprehensive approach for complex study designs.

Clinical Trial Design (e.g., Innovative/Complex Design, Estimands, Master Protocol)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024