Bayesian predictive probabilities: a good way to monitor clinical trials

David Ferreira, Pierre Olivier Ludes, Pierre Diemunsch, Eric Noll, Klaus D. Torp, Nicolas Meyer

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Bayesian methods, with the predictive probability (PredP), allow multiple interim analyses with interim posterior probability (PostP) computation, without the need to correct for multiple looks at the data. The objective of this paper was to illustrate the use of PredP by simulating a sequential analysis of a clinical trial. Methods: We used data from the Laryngobloc trial that planned to include 480 patients to demonstrate the equivalence of success between a laryngoscopy performed with the Laryngobloc® device and a control device. A crossover Bayesian design was used. The success rates of the two laryngoscopy devices were compared. Interim analyses, computed from random numbers of subjects, were simulated. Results: The PostP of equivalence rapidly reached the predefined bound of 0.95. The PredP computed with an equivalence margin of 10% reached the efficacy bound between 352 and 409 of the 480 included patients. If a frequentist analysis had been made on the basis of 217 out of 480 subjects, the study would have been prematurely stopped for equivalence. The PredP indicated that this result was nonetheless unstable and that the equivalence was, thus far, not guaranteed. Conclusions: Based on these interim analyses, we can conclude with a sufficiently high probability that the equivalence would have been met on the primary outcome before the predetermined end of this particular trial. If a Bayesian approach using PredP had been used, it would have allowed an early termination of the trial by reducing the calculated sample size by 15–20%.

Original languageEnglish (US)
Pages (from-to)550-555
Number of pages6
JournalBritish journal of anaesthesia
Volume126
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Bayesian statistics
  • RCT
  • clinical trial
  • monitoring
  • predictive probabilities
  • statistics

ASJC Scopus subject areas

  • Anesthesiology and Pain Medicine

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