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

Research output: Contribution to journalArticle

Authors

  • Julie Bruce
  • Andrew Metcalfe
  • Nigel Stallard
  • Sarah Lamb
  • Kert Viele
  • Duncan Young

Colleges, School and Institutes

External organisations

  • Warwick Clinical Trials Unit, University of Warwick, Coventry
  • Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
  • Centre for Rehabilitation Research and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics Rheumatology & Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
  • Department of Trauma and Orthopaedic Surgery, University Hospital Coventry & Warwick, Coventry, UK
  • Berry Consultants, Austin, Texas, USA
  • Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
  • Warwick Clinical Trials Unit, Coventry, UK.

Abstract

Background: 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. 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.

Methods: We 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.

Results: We 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.

Conclusions: Using 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 registration: OSCAR Trial registration ISRCTN, ISRCTN10416500. Registered 13 June 2007

Details

Original languageEnglish
Article number99
JournalBMC Medical Research Methodology
Volume19
Publication statusPublished - 14 May 2019

Keywords

  • Bayesian sequential design, interim analyses, randomized controlled trials, critical care