Arlene Jiang | Searcharxiv

arXiv2026

Remote, bivariate expert elicitation to determine the prior probability distribution for sample size calculation in a Bayesian non-inferiority multicenter randomized controlled trial (Croup Dosing Trial)

Prior distributions must be specified for the parameters of interest in a Bayesian clinical trial. When existing evidence on the effects of the trial interventions is limited, prior distributions can be constructed with expert elicitation. However, conventional elicitation requires face-to-face interactions and intensive pre-elicitation training, which can be infeasible. Our remote elicitation was based on established expert elicitation methods. We used bivariate prior distributions for dependencies between elicited quantities. We elicited a prior distribution for the Croup Dosing Trial, which will assess the number of return visits to the emergency department within 7 days in children with croup. This trial evaluates the non-inferiority of 0.15 mg/kg of dexamethasone, compared to the standard dose of 0.60 mg/kg to treat croup. We conducted three remote workshops to elicit expert beliefs on the efficacy of the two doses of dexamethasone. Each workshop consisted of two survey rounds, separated by a group discussion. Prior to the workshop, experts reviewed provided literature on the effects of the two doses of dexamethasone. Beliefs were aggregated with expert-specific bivariate distributions. The aggregated distribution and surveyed non-inferiority margin determined the sample size. Twelve emergency medicine physicians participated in our remote elicitation exercise. The elicitation generated a prior distribution centered at 6% for the 0.60 mg/kg dose and 8% for the 0.15 mg/kg dose. The aggregated prior distribution produced a sample size of 1850, based on a non-inferiority margin of 4%. We elicited a prior distribution that incorporated past evidence and expert opinion. The elicited prior is consistent with literature on the efficacy of the dexamethasone doses in treating croup. Our approach demonstrates the feasibility of remotely eliciting bivariate distributions for clinical trials.

stat.AP↗

arXiv2025

A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used

Utilizing Bayesian methods in clinical trials has become increasingly popular, as they can incorporate historical data and expert opinions into the design and allow for smaller sample sizes to reduce costs while providing reliable and robust statistical results. Sample size determination (SSD) is a key aspect of clinical trial design and various methods for Bayesian sample size determination are available. However, it is unclear how these methods are being used in practice. A systematic literature review was conducted to understand how sample sizes for Bayesian randomized clinical trials (RCTs) are determined and inform the design of future Bayesian trials. We searched five databases in May 2023, and updated in January 2025, including efficacy RCTs in humans which utilized a Bayesian framework for the primary data analysis, published in English, and enrolled participants between 2009 and 2024. The literature search produced 19,182 records, of which 105 studies were selected for data extraction. Results show that the most common method for SSD in Bayesian RCTs was a hybrid approach in which elements of Bayesian and frequentist theory are combined. Many RCTs did not provide a justification for SSD, while fully Bayesian methods were rarely used in practice, despite significant theoretical development. Our review also revealed a lack of standardized reporting, making it challenging to review the SSD. The CONSORT statement for reporting RCTs states that sample size calculations must be reported, which was poorly adhered to. Among RCTs that reported SSD, relevant information was frequently omitted from the reports and discussed in poorly structured supplementary materials. Thus, there is a critical need for greater transparency, standardization and translation of relevant methodology in Bayesian RCTs.

stat.AP↗