Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Autor: Ram C. Tiwari, H. Amy Xia, Guochen Song, Mathangi Gopalakrishnan, Xin Zhao, Laura Thompson, Bradley P. Carlin, Simin K. Baygani, Karen L. Price, Aijun Gao, Cynthia Basu, Jasmina Savic, Margaret Gamalo-Siebers
Rok vydání: 2017
Předmět:
Zdroj: Pharmaceutical Statistics. 16:232-249
ISSN: 1539-1604
DOI: 10.1002/pst.1807
Popis: Children represent a large underserved population of "therapeutic orphans," as an estimated 80% of children are treated off-label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or "borrowing") of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure-response information for antiepileptic drugs to pediatrics.
Databáze: OpenAIRE
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