ABSTRACT
This chapter reviews of existing classical approaches to data extrapolation, and develops Bayesian hierarchical approaches to the problem. An increasingly well-accepted approach to address these challenges has been data extrapolation; that is, the leveraging of available data from adults or older age groups to help draw conclusions for the pediatric population. The paucity of potential clinical trial enrollees and sensitivity of these patients, combined with a lack of sufficient natural history and experience, presents several economical, logistical and ethical challenges when designing trials for these populations. The traditional approach to analyzing a trial for pediatric diseases, where the drug has already been approved for adults, is to carry out a study on children and analyze it without any information borrowed from adult data. A slightly more sophisticated and flexible method to facilitate data extrapolation while retaining control on the amount of borrowing is through the use of the power prior.
