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. PURPOSE: 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. LIMITATIONS: 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.
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. PURPOSE: 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. LIMITATIONS: 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.
Authors: Roger G Spragg; Gordon R Bernard; William Checkley; J Randall Curtis; Ognjen Gajic; Gordon Guyatt; Jesse Hall; Elliott Israel; Manu Jain; Dale M Needham; Adrienne G Randolph; Gordon D Rubenfeld; David Schoenfeld; B Taylor Thompson; Lorraine B Ware; Duncan Young; Andrea L Harabin Journal: Am J Respir Crit Care Med Date: 2010-03-11 Impact factor: 21.405
Authors: Stacey L Valentine; Anil Sapru; Renee A Higgerson; Phillip C Spinella; Heidi R Flori; Dionne A Graham; Molly Brett; Maureen Convery; LeeAnn M Christie; Laurie Karamessinis; Adrienne G Randolph Journal: Crit Care Med Date: 2012-10 Impact factor: 7.598
Authors: Martina Penazzato; Devasena Gnanashanmugam; Pablo Rojo; Marc Lallemant; Linda L Lewis; Francesca Rocchi; Agnes Saint Raymond; Nathan Ford; Rohan Hazra; Carlo Giaquinto; Yodit Belew; Diana M Gibb; Elaine J Abrams Journal: Clin Infect Dis Date: 2017-06-01 Impact factor: 9.079