A Randomized Dose-Ranging Basket Trial Design with Enrichment for Dose Optimization

Basket trials provide a promising framework for evaluating treatment efficacy across multiple indications, yet many conventional designs fail to account for patient heterogeneity or assume uniform responses across all patients. In this study, we propose a randomized basket trial design with enrichment that addresses these limitations by selectively recruiting treatment-sensitive patients and leveraging a hierarchical Bayesian model (HBM) for dose optimization. Through extensive simulations, we demonstrate that our design outperforms traditional approaches—both fully pooled and independent designs—by improving estimation efficiency without sacrificing indication-specific accuracy. Specifically, enrichment-based strategies (Ind-E, Pool-E, HBM-E) yield more accurate dose selection and higher decision-making power in heterogeneous populations, effectively balancing efficiency and flexibility. The incorporation of HBM allows for adaptive information sharing across indications, further enhancing statistical precision. Overall, our proposed design provides a robust and efficient framework for identifying optimal biologic doses in basket trials, paving the way for more personalized and precise clinical decision-making.