Cohort expansion trials where multiple dose(s) and multiple indication(s) need to be tested in parallel can present complex problems for statisticians. The solution to these problems lie in MuCE: methods for multiple cohort expansion. Built on Bayesian hierarchical models with multiplicity control, MUCE adaptively borrows information across patient groups from different indications treated with different doses. Such methods are particularly important for areas like oncology where several doses and several indications must be tested for successful completion of early phase trials, and optimal choice of dose and population to move on from early phase to a reasonable dosage for Phase 3. Find out more about MuCE in Cytel’s latest blog entry here.