  We propose a multiple cohort expansion (MUCE) approach as a design or
analysis method for phase 1b multiple expansion cohort trials, which are novel
first-in-human studies conducted following phase 1a dose escalation. The MUCE
design is based on a class of Bayesian hierarchical models that adaptively
borrow information across arms. Statistical inference is directly based on the
posterior probability of each arm being efficacious, facilitating the decision
making that decides which arm to select for further testing.
