  Adaptive approaches, allowing for more flexible trial design, have been
proposed for individually randomized trials to save time or reduce sample size.
However, adaptive designs for cluster-randomized trials in which groups of
participants rather than individuals are randomized to treatment arms are less
common. Motivated by a cluster-randomized trial designed to assess the
effectiveness of a machine-learning based clinical decision support system for
physicians treating patients with depression, two Bayesian adaptive designs for
cluster-randomized trials are proposed to allow for early stopping for efficacy
at pre-planned interim analyses. The difference between the two designs lies in
the way that participants are sequentially recruited. Given a maximum number of
clusters as well as maximum cluster size allowed in the trial, one design
sequentially recruits clusters with the given maximum cluster size, while the
other recruits all clusters at the beginning of the trial but sequentially
enrolls individual participants until the trial is stopped early for efficacy
or the final analysis has been reached. The design operating characteristics
are explored via simulations for a variety of scenarios and two outcome types
for the two designs. The simulation results show that for different outcomes
the design choice may be different. We make recommendations for designs of
Bayesian adaptive cluster-randomized trial based on the simulation results.
