A Bayesian approach to randomized controlled trials in children utilizing information from adults: The case of Guillain-Barré syndrome

Background: Guillain-Barré syndrome (GBS) is a rare neurologic disease that occurs at all ages, causing a progressive, ascending paralysis that usually resolves over weeks or months. The disease appears to be identical in children and adults, except that children recover more quickly, with fewer residua. For patients who lose the ability to walk independently, the main treatment options are plasmapheresis or intravenous immune globulin (IVIg), treatments that have shown to have identical effectiveness in adults in two large RCTs involving 388 patients. The effectiveness of the treatments in children has only been studied in small, poorly controlled studies. If one could capture all eligible patients in the United States, only about 100-300 children would be available for a trial annually. Methods: The goal of this case was to demonstrate how Bayesian methods could be used to incorporate prior information on treatment efficacy from adults to design a randomized noninferiority trial of IVIg versus plasmapheresis in children. A Bayesian normal-normal model on the hazard ratio of time to independent walking was implemented. Results: An evidence-based prior was constructed that was equivalent to 72 children showing exact equivalence between the therapies. A design was constructed based on a Bayesian normal-normal model on the hazard ratio, yielding a sample size of 160 children, with a preposterior analysis demonstrating a "Type I" error rate of 5% and a power of 77%. Conclusions: This case study illustrates a rational approach to constructing an evidence-based prior that would allow information from adults to formally augment data from children to minimize unnecessary pediatric experimentation. The frequentist properties of a Bayesian design can be evaluated and reported as they would be for a standard design. Discussion of the appropriate prior for such designs is both a necessary and desirable feature of Bayesian trials.