Remote, real-time expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomised controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) study

Developments in the Design, Conduct, and Reporting of Child Health Trials

To identify priority areas to improve the design, conduct, and reporting of pediatric clinical trials, the international expert network, Standards for Research (StaR) in Child Health, was assembled and published the first 6 Standards in Pediatrics in 2012. After a recent review summarizing the 247 publications by StaR Child Health authors that highlight research practices that add value and reduce research "waste," the current review assesses the progress in key child health trial methods areas: consent and recruitment, containing risk of bias, roles of data monitoring committees, appropriate sample size calculations, outcome selection and measurement, and age groups for pediatric trials. Although meaningful change has occurred within the child health research ecosystem, measurable progress is still disappointingly slow. In this context, we identify and review emerging trends that will advance the agenda of increased clinical usefulness of pediatric trials, including patient and public engagement, Bayesian statistical approaches, adaptive designs, and platform trials. We explore how implementation science approaches could be applied to effect measurable improvements in the design, conducted, and reporting of child health research.

A Decade of Efforts to Add Value to Child Health Research Practices

OBJECTIVE

To identify practices that add value to improve the design, conduct, and reporting of child health research and reduce research waste.

STUDY DESIGN

In order to categorize the contributions of members of Standards for Research (StaR) in Child Health network, we developed a novel Child Health Improving Research Practices (CHIRP) framework comprised of 5 domains meant to counteract avoidable child health research waste and improve quality: 1) address research questions relevant to children, their families, clinicians, and researchers; 2) apply appropriate research design, conduct and analysis; 3) ensure efficient research oversight and regulation; 4) Provide accessible research protocols and reports; and 5) develop unbiased and usable research reports, including 17 responsible research practice recommendations. All child health research relevant publications by the 48 original StaR standards' authors over the last decade were identified, and main topic areas were categorized using this framework.

RESULTS

A total of 247 publications were included in the final sample: 100 publications (41%) in domain 1 (3 recommendations), 77 publications (31%) in domain 2 (3), 35 publications (14%) in domain 3 (4), 20 publications (8%) in domain 4 (4), and 15 publications (6%) in domain 5 (3). We identified readily implementable "responsible" research practices to counter child health research waste and improve quality, especially in the areas of patients and families' engagement throughout the research process, developing Core Outcome Sets, and addressing ethics and regulatory oversight issues.

CONCLUSION

While most of the practices are readily implementable, increased awareness of methodological issues and wider guideline uptake is needed to improve child health research. The CHIRP Framework can be used to guide responsible research practices that add value to child health research.

A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines.