Innovative Methods for Rare Disease Drug Development - Chow, Shein-Chung
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In the United States, a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200,000 persons. For the approval of "orphan" drug products for rare diseases, the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case, innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development…mehr

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Produktbeschreibung
In the United States, a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200,000 persons. For the approval of "orphan" drug products for rare diseases, the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case, innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development focuses on biostatistical applications in terms of design and analysis in pharmaceutical research and development from both regulatory and scientific (statistical) perspectives.

Key Features:

Reviews critical issues (e.g., endpoint/margin selection, sample size requirements, and complex innovative design).

Provides better understanding of statistical concepts and methods which may be used in regulatory review and approval.

Clarifies controversial statistical issues in regulatory review and approval accurately and reliably.

Makes recommendations to evaluate rare diseases regulatory submissions.

Proposes innovative study designs and statistical methods for rare diseases drug development, including n-of-1 trial design, adaptive trial design, and master protocols like platform trials.

Provides insight regarding current regulatory guidance on rare diseases drug development like gene therapy.

Autorenporträt
Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.
Rezensionen
The very limited sample size of patients with rare disease brings a lot of challenges in both design and analysis of clinical trials as compared to the other common disease. To promote rare diseases drug development, innovative thinking is not only encouraged by FDA but also needed for the pharmaceutical companies. This book is published in a very timely manner that enable the promoting of these innovative design and analysis. I believe that it will certainly not only inspire the statisticians working in rare disease, but also could shed some light on the solutions of unique situation for clinical trials with common disease. Therefore, I strongly recommend this book to all the statisticians who work on clinical trials, not only those in rare disease but also on other indications.

- Meijing Wu, Journal of Biopharmaceutical Statistics, August 2021

"I recommend this book to researchers who want to delve into the world of rare-disease trials, and in the meanwhile would encourage them to actively think about the problems also from a Bayesian perspective."

- Haiyan Zheng, International Society for Clinical Biostatistics, 72, 2021