Background: Bayesian adaptive designs can be more efficient than traditional methods for multi‐arm
randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive
designs can be constructed for multi‐arm phase III clinical trials and assess potential benefits that
these designs offer.
Methods: We constructed ...
Background: Bayesian adaptive designs can be more efficient than traditional methods for multi‐arm
randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive
designs can be constructed for multi‐arm phase III clinical trials and assess potential benefits that
these designs offer.
Methods: We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle
Support Trial (CAST), which was a randomised controlled trial that compared four treatments for
severe ankle sprain. These incorporated response adaptive randomisation, arm dropping, and early
stopping for efficacy or futility. We studied the Bayesian designs’ operating characteristics via
simulation. We then virtually re‐executed the trial by implementing the Bayesian adaptive designs
using patient data sampled from the CAST study to demonstrate the practical applicability of the
designs.
Results: We constructed five Bayesian adaptive designs, each of which had high power and recruited
fewer patients on average than the original design’s target sample size. The virtual executions
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showed that most of the Bayesian designs would have led to trials that declared superiority of one of
the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more
likely to allocate patients to better performing arms at each interim analysis. Similar estimates and
conclusions were obtained from the Bayesian adaptive designs as from the original trial.
Conclusions: Using CAST as an example, this case study showed how Bayesian adaptive designs can
be constructed for phase III multi‐arm trials using clinically relevant decision criteria. These designs
demonstrated that they can potentially generate earlier results and allocate more patients to betterperforming
arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical
trials.