Bayesian adaptive designs for multi-arm trials: an orthopaedic case study

Abstract

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 2 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.