Remote expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomized controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) Study

Abstract

BackgroundBayesian methods are increasing in popularity in clinical research. The design of Bayesian clinical trials requires a prior distribution, which can be elicited from experts. Current elicitation approaches either use face-to-face sessions or expert surveys. In diseases with international differences in management, the elicitation exercise should recruit internationally, requiring expensive face-to-face sessions or surveys, which suffer low response rates. To address this, we developed a remote, real-time elicitation exercise to construct prior distributions. These elicited distributions were then used to determine the sample size of the Bronchiolitis in Infants with Placebo Versus Epinephrine and Dexamethasone (BIPED) Study, an international randomized controlled trial trial in the Pediatric Emergency Research Network (PERN). The BIPED study aims to determine whether the combination of epinephrine and dexamethasone, compared to placebo, is effective in reducing hospital admission for infants presenting with bronchiolitis to the emergency department.MethodsWe developed a web-based tool to support the elicitation of the probability of hospitalization for infants with bronchiolitis. Experts participated in online workshops to specify their individual prior distributions, which were aggregated using the equal-weighted linear pooling method. The Average Length Criterion determined the BIPED sample size.ResultsFifteen paediatric emergency medicine clinicians from Canada, USA, Australia and New Zealand participated in three workshops to provide their elicitied prior distributions. The elicited probability of admission for infants with bronchiolitis was slightly lower for those receiving epinephrine and dexamethasone compared to supportive care in the aggregate distribution. There were substantial differences in the individual beliefs but limited differences between North America and Australaisia. From this aggregate distribution, a sample size of 410 patients per arm results in an average 95% credible interval length of less than 9% and a relative predictive power of 90%.ConclusionRemote expert elicitation is a feasible, useful and practical tool to determine a prior distribution for international randomized controlled trials. Bayesian methods can then determine the trial sample size using these elicited prior distributions. The ease and low cost of remote expert elicitation means that this approach is suitable for future international randomized controlled trials.Trial Registration: clinicaltrials.gov Identifier: NCT03567473

The Probability of Reducing Hospitalization Rates for Bronchiolitis: A Bayesian Analysis

“…The use of Bayesian methods circumvents a discussion on whether the analysis should be adjusted for multiple comparisons, which previously complicated the interpretation of CanBEST. Note that due to the uncertain interpretation of CanBEST in the frequentist paradigm, an international randomized trial is currently underway to answer the calls for further evidence on the effectiveness of the combined therapy (40). Based on our analysis, the results from this trial would be most relevant if clinicians were initially highly skeptical about the effectiveness of the combined therapy and are, thus, still unconvinced by CanBEST.…”