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We propose a multiple cohort expansion (MUCE) approach as a design oranalysis method for phase 1b multiple expansion cohort trials, which are novelfirst-in-human studies conducted following phase 1a dose escalation. The MUCEdesign is based on a class of Bayesian hierarchical models that adaptivelyborrow information across arms. Statistical inference is directly based on theposterior probability of each arm being efficacious, facilitating the decisionmaking that decides which arm to select for further testing.

MUCE: Bayesian Hierarchical Modeling for the Design and Analysis of Phase 1b Multiple Expansion Cohort Trials