Dr. Birol Emir's research primarily focuses on big data, predictive modeling, and genomics data analysis. He has numerous publications in refereed journals, has taught several short courses, and has given invited presentations. He recently co-authored a new book as part of the Chapman & Hall/CRC Biostatistics Series, Interface Between Regulation and Statistics in Drug Development.
With the critical role of statistics in the design, conduct, analysis and reporting of clinical trials or observational studies intended for regulatory purposes, numerous guidelines have been issued by regulatory authorities around the world focusing on statistical issues related to drug development. However, the available literature on this important topic is sporadic, and often not readily accessible to drug developers or regulatory personnel. This book provides a systematic exposition of the interplay between the two disciplines, including emerging themes pertaining to the acceleration of the development of pharmaceutical medicines to serve patients with unmet needs.
The book covers:
- Regulatory and statistical interactions throughout the drug development continuum
- The critical role of the statistician in relation to the changing regulatory and healthcare landscapes
- Statistical issues that commonly arise in the course of drug development and regulatory interactions
- Trending topics in drug development, with emphasis on current regulatory thinking and the associated challenges and opportunities
The book is designed to be accessible to readers with an intermediate knowledge of statistics, and can be a useful resource to statisticians, medical researchers, and regulatory personnel in drug development, as well as graduate students in the health sciences. The authors’ decades of experience in the pharmaceutical industry and academia, and extensive regulatory experience, comes through in the many examples throughout the book.
The book is currently available for purchase on Amazon.
The views expressed are those of the author and do not necessarily represent the views of any other person or entity.