Application of Bayesian statistics to support approval of intravenous belimumab in children with systemic lupus erythematosus in the United States.

Abstract

ObjectiveDue to the rarity of childhood-onset systemic lupus erythematosus (cSLE), the pediatric study (NCT01649765) conducted to support approval of intravenous (IV) belimumab in that population could not be designed with adequate power for efficacy to make statistical inferences based solely on the pediatric data. Bayesian analyses have been proposed to implement more flexible pediatric extrapolation approaches in similar situations. Here we discuss a post-hoc Bayesian analysis the FDA employed which borrowed information from the phase 3 IV belimumab studies in adults to supplement the efficacy information collected in the pediatric population.MethodsWe used a Bayesian method to estimate the treatment effect in SLE Responder Index (SRI) response in pediatric patients, with a robust mixture prior that was a weighted combination of a skeptical prior that assumed no treatment effect and a meta-analysis of the treatment effects seen in adults. The weighting was based on the degree of belief in the similarity of the disease and treatment effect between pediatric and adult subjects.ResultsThe trial design and conduct, endpoints, key demographic and disease characteristics as well as pharmacokinetics (PK) were similar between the pediatric and adult study populations, which justified borrowing information from across studies. A prior weight of 55% or larger on the adult data provided 95% credible intervals that excluded an odds ratio of one, providing supportive evidence that belimumab is an effective treatment in pediatric patients. These results were further supported by the secondary efficacy endpoints, PK and safety.ConclusionThis approach contributed to FDA's decision to approve belimumab as the first treatment for cSLE. If pre-specified, such an approach may help expedite future clinical development in pediatric rheumatic diseases, and address some of the challenges with conducting trials for rare diseases.