We present a novel Bayesian adaptive phase 1 design to determine the optimal dosing regimen for an adoptive T-cell therapy in a mixed patient population. Our design is motivated by a B-cell Non-Hodgkin Lymphoma trial evaluating multiple dosing regimens within multiple disease subtypes. A utility score is calculated from both safety and efficacy utility functions and used to guide dose-escalation decisions. We pool safety data across disease subtypes and use a single dose-toxicity model while sharing efficacy information between disease subtypes using a hierarchical dose-response model. In addition, an adaptive randomization approach is applied to dynamically assign patients to a regimen when more than one regimen is open for enrollment. We illustrate this study design through a simulated trial example, and we investigate the operating characteristics using simulation studies.
Keywords: Adaptive randomization; Bayesian adaptive; Hierarchical modeling; Mixed population; Phase 1; Utility score.
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