Empirical profile Bayesian estimation for extrapolation of historical adult data to pediatric drug development

For pediatric drug development, the clinical effectiveness of the study medication for the adult population has already been demonstrated. Given the fact that it is usually not feasible to enroll a large number of pediatric patients, appropriately leveraging historical adult data into pediatric evaluation may be critical to success of pediatric drug development. In this manuscript, we propose a new empirical Bayesian approach, profile Bayesian estimation, to dynamically borrow adult information to the evaluation of treatment effect in pediatric patients. The new approach demonstrates an attractive balance between type I error control and power gain under the transfer-ability assumption that the pediatric treatment effect size may differ from the adult treatment effect size. The decision making boundary mimics the real-world practice in pediatric drug development. In addition, the posterior mean of the proposed empirical profile Bayesian is an unbiased estimator of the true pediatric treatment effect. We compare our approach to robust mixture prior with prior weight for informative borrowing set to 0.5 or 0.9, regular Bayesian approach, and frequentist for both type I error and power.