Bayesian design using adult data to augment pediatric trials.
BACKGROUND
It can be difficult to conduct pediatric clinical trials because there is often a low incidence of the disease in children, making accrual slow or infeasible. In addition, low mortality and morbidity in this population make it impractical to achieve adequate power. In this case, the only evidence for treatment efficacy comes from adult trials. Since pediatric care providers are accustomed to relying on evidence from adult studies, it is natural to consider borrowing information from adult trials.


OBJECTIVE
The goal of this article is to propose a Bayesian approach to the design and analysis of pediatric trials to allow borrowing strength from previous or simultaneous adult trials.


METHODS
We apply a hierarchical model for which the efficacy parameter from the adult trial and that of the pediatric trail are considered to be draws from a normal distribution. The choice of (the variance of) this distribution is guided by discussion with medical experts. We show that with this information, one can calculate the sample size required for the pediatric trial. We discuss how inference of these studies in pediatric populations depends on the parameter that captures the similarity of the treatment efficacy in adults compared to children.


RESULTS
The Bayesian approach can substantially increase the power of a pediatric clinical trial (or equivalently decrease the number of subjects required) by formally leveraging the data from the adult trial.


CONCLUSIONS
Our method relies on obtaining a value for the inter-study variability, nu, which may be difficult to describe to a clinical investigator.


CONCLUSIONS
The Bayesian approach has the potential of making pediatric clinical trials feasible because it has the effect of borrowing strength from adult trials, thus requiring a smaller pediatric trial to show efficacy of a drug in children.
Schoenfeld
David A
DA
Massachusetts General Hospital, Boston, MA, USA.
Hui Zheng
Finkelstein
Dianne M
DM
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D001499:Bayes Theorem; D002648:Child; D002986:Clinical Trials as Topic; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012106:Research; D012107:Research Design; D018401:Sample Size; D055815:Young Adult