Illustrating how stability studies play an important role in drug safety and quality assurance, this book introduces the basic concepts of stability testing, focuses on short-term stability studies, and reviews several methods for estimating drug expiration dating periods. The author compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. The book examines the stability analysis of a linear mixed effects model and stability analyses with discrete responses, multiple components, and frozen drug products. It also explores dissolution testing, current issues, and recent developments in stability studies.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
"...This book provides a comprehensive and unified presentation of the principles and methodologies of design and analysis of stability studies. The text is well-written and provides a well-balanced summary of current regulatory perspectives. The primary SAS programs (macros) -comprising STAB-for estimating the expiration dating period of a drug product based on linear regression analysis are listed in the Appendix B and these macros help to reinforce the theory presented in the text. ..."
-International Statistical Review, 2008
". . . serves as a useful reference for those who are interested in the statistical design and analysis of stability studies in drug research and development. Its clear explanation, plentiful illustrations, moderate mathematical exposition, and inclusion of SAS programming codes make all the materials accessible to most of the applied statisticians working in the biopharmaceutical environment."
- Ying Qing Chen, Fred Hutchinson Cancer Research Center, in JASA, March 2009
-International Statistical Review, 2008
". . . serves as a useful reference for those who are interested in the statistical design and analysis of stability studies in drug research and development. Its clear explanation, plentiful illustrations, moderate mathematical exposition, and inclusion of SAS programming codes make all the materials accessible to most of the applied statisticians working in the biopharmaceutical environment."
- Ying Qing Chen, Fred Hutchinson Cancer Research Center, in JASA, March 2009