Abstract
BackgroundBayesian 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.MethodsWe 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 designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs 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.ResultsWe constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions 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.ConclusionsUsing CAST as an example, this case study shows 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 better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Trial registrationCAST study registration ISRCTN, ISRCTN37807450. Retrospectively registered on 25 April 2003.
Highlights
Bayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials
Case study The Collaborative Ankle Support Trial (CAST; [15,16,17]) was a phase III pragmatic, individually randomised controlled trial (RCT) that compared the effectiveness of three types of mechanical ankle support with tubular bandage for patients with severe ankle sprains
We found that performing response adaptive randomisation (RAR) or arm dropping more frequently increased the probability of trial success and decreased the average sample size, and so we only present the adaptive designs that performed RAR or arm dropping every 50 patients
Summary
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. The traditional phase III trial design generally involves randomising patients to one of two arms, often with equal probability of allocation and using fixed sample sizes. Phase III trials generally require large sample sizes, have long duration, and many are declared “unsuccessful” due to a perceived lack of difference between treatment arms [1]. Adaptive trial designs have the potential to allow trials to answer their questions more efficiently, for multi-arm trials, by enabling design components to be altered based on analyses of accumulated data.
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