Using Bayesian adaptive designs to improve phase III trials: a respiratory care example

Elizabeth G. Ryan,Nigel Stallard,Andrew J. Metcalfe,Julie Bruce,Simon Gates,Sarah E. Lamb,Duncan Young,Kert Viele

doi:10.1186/s12874-019-0739-3

Abstract

BackgroundBayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for phase III clinical trials in critical care, and to assess the influence that Bayesian designs would have on trial efficiency and study results.MethodsWe re-designed the High Frequency OSCillation in Acute Respiratory distress syndrome (OSCAR) trial using Bayesian adaptive design methods, to allow for the possibility of early stopping for success or futility. We constructed several alternative designs and studied their operating characteristics via simulation. We then performed virtual re-executions by applying the Bayesian adaptive designs using the OSCAR data to demonstrate the practical applicability of the designs.ResultsWe constructed five alternative Bayesian adaptive designs and identified a preferred design based on the simulated operating characteristics, which had similar power to the original design but recruited fewer patients on average. The virtual re-executions showed the Bayesian sequential approach and original OSCAR trial yielded similar trial conclusions. However, using a Bayesian sequential design could have led to a reduced sample size and earlier completion of the trial.ConclusionsUsing the OSCAR trial as an example, this case study found that Bayesian adaptive designs can be constructed for phase III critical care trials. If the OSCAR trial had been run using one of the proposed Bayesian adaptive designs, it would have terminated at a smaller sample size with fewer deaths in the trial, whilst reaching the same conclusions. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Trial registrationOSCAR Trial registration ISRCTN, ISRCTN10416500. Retrospectively registered 13 June 2007.

Highlights

Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods
Using an example from a recent critical care trial, we demonstrate how a Bayesian sequential design can be constructed, and illustrate the choices required during the design phase
Under the target difference of a 9% reduction in 30-day mortality, the average sample sizes were reduced by approximately 200–300 patients

Summary

Introduction

Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. Phase III randomised controlled trials (RCTs) are typically long and expensive, restricting their use and resulting in long lead times to answer important clinical questions [1]. Traditional phase III design methods require specification of the sample size in advance. Sequential designs can offer a more efficient approach for conducting RCTs and frequently result, on average, in smaller and shorter trials than traditional approaches. Sequential designs may be implemented using frequentist methods, which typically use null hypothesis testing, or Bayesian methods

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