Bayesian Design for Pediatric Clinical Trials with Binary Endpoints When Borrowing Historical Information of Treatment Effect

The efficacy evaluation in pediatric population is an important component of drug development and is generally required by the regulatory agencies. It is often challenging to enroll pediatric subjects for a large trial especially when the incidence rate is low in certain disease areas. Bayesian framework can provide analytic avenues to effectively utilize historical information of the treatment effect and help make pediatric trials more efficient by reducing the sample size when there is evidence to suggest similarity of the treatment responses between the populations. Schoenfeld et al. (Clin Trials 6(4):297–304, 2009) proposed a Bayesian hierarchical model for efficacy extrapolation for continuous endpoints, which connects a single historical trial and the current trial by a variance parameter in the prior distribution. In this manuscript, we extend the existing model to borrow strength from multiple historical trials under the same assumptions and develop a quantitative method to borrow historical information more efficiently. Furthermore, we extend Schoenfeld’s method based on continuous endpoints to binary endpoints with a hierarchical binomial model to extrapolate efficacy. Sensitivity analyses for the underlying assumptions are discussed with simulations and the methods are illustrated with a real case study, along with some practical considerations about how to choose the prior distribution.