| U.S. regulatory considerations and case studies for rare diseases In this talk, I will present an overview of the U.S. Food and Drug Administration’s policies and practices for encouraging development of products for rare diseases and of evaluating clinical evidence for the safety and effectiveness of such products. I’ll discuss study designs that may be particularly appropriate for rare disease product development, and address some of their statistical implications. Finally, I’ll present case studies of products that were approved for rare diseases using unusual or innovative study designs and/or regulatory pathways. |
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John Scott is Deputy Director of the Division of Biostatistics in the FDA's Center for Biologics Evaluation and Research, where he has also served as a statistical reviewer for blood products and for cellular, tissue and gene therapies. Prior to joining the FDA in 2008, he worked in psychiatric clinical trials at the University of Pittsburgh Medical Center and did neuroimaging research with the Neurostatistics Laboratory at McClean Hospital, Harvard Medical School. He has authored or co-authored numerous articles in areas including Bayesian and adaptive clinical trial design and analysis, drug and vaccine safety, data and text mining, and benefit-risk assessment. He holds a Ph.D. in Biostatistics from the University of Pittsburgh and an M.A. in Mathematics from Washington University in St. Louis, and is an associate editor of the journal, Pharmaceutical Statistics. |
| Bayesian methods for the design and interpretation of clinical trials in rare diseases For studies in rare diseases, the sample size needed to meet a conventional frequentist power requirement can be daunting, even if patients are to be recruited over several years. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose Bayesian approaches for the conduct of rare disease trials comparing an experimental treatment with a control when the primary endpoint is binary or normally distributed. We describe processes which can be used to systematically elicit from clinicians opinions on treatment efficacy in order to establish Bayesian priors for unknown model parameters. The proposed approaches are illustrated by describing applications to two Bayesian randomised controlled trials, namely a study in childhood polyarteritis nodosa and a study in chronic recurrent multifocal osteomyelitis. Once prior distributions have been established, consideration of the extent to which opinion can be changed, even by the best feasible design, can help to determine whether a small trial is worthwhile. |
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Lisa Hampson is a Lecturer in Statistics at Lancaster University. Her research interests are in clinical trials, including group sequential tests and Bayesian methods for trials in rare diseases and dose-escalation. Her recent research has focused on developing methods for clinical trials of new medicines for children. She holds a PhD in Statistics from the University of Bath. |
