Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation |
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| Authors: | Margaret Gamalo‐Siebers Jasmina Savic Cynthia Basu Xin Zhao Mathangi Gopalakrishnan Aijun Gao Guochen Song Simin Baygani Laura Thompson H. Amy Xia Karen Price Ram Tiwari Bradley P. Carlin |
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| Affiliation: | 1. Advanced Analytics, Eli Lilly & Co, Lilly Corporate Center, Indianapolis, IN, USA;2. JS Regulatory, Aachen, Germany;3. Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA;4. Biostatistics, Johnson & Johnson, South San Francisco, CA, USA;5. Research Assistant Professor, Center for Translational Medicine, University of Maryland, Baltimore, MD, USA;6. Biostatistics, Chiltern International Ltd., King of Prussia, PA, USA;7. Biogen, Cambridge, MA, USA;8. Global Statistical Science, Eli Lilly & Co, Lilly Corporate Center, Indianapolis, IN, USA;9. Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA;10. Biostatistics, Amgen, Thousand Oaks, CA, USA;11. Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA |
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| Abstract: | ![]()
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. |
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| Keywords: | commensurate prior exchangeability extrapolation effective sample size hierarchical model model fit power prior |
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