Olga V. Marchenko (editor) - Quantitative Methods in Pharmaceutical Research and Development: Concepts and Applications

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The motivation for this book came from a discussion on how to help a young individual interested in quantitative disciplines in school to choose a major for further education and a career. In the old days, if someone were good in quantitative disciplines in school, he or she would go to college to receive a degree in Mathematics or Physics. Nowadays, universities offer a range of different concentration areas that rely on quantitative methods, such as Mathematics, Statistics, Biostatistics, Pharmacometrics, Genetics, Computer Science, Data Science, to name a few. It is not easy to make an educated choice for a future career. We decided to focus on the pharmaceutical industry specifically. There are many books available that describe one specific area, e.g., statistics, or a couple of areas, but we are not aware of books that provide a good overview of different analytics and statistical applications used in the pharmaceutical industry. Additionally, we were trying to understand what quantitative methods different departments at a company use to answer questions in the pharmaceutical industry and how people working at these departments collaborate and build on each others knowledge.

The book Quantitative Methods in Pharmaceutical Research and Development presents an overview of concepts, methods, and applications in different quantitative areas of drug research, development, and marketing. Biostatistics, pharmacometrics, genomics, bioinformatics, pharmacoepidemiology, commercial analytics, and operational analyticsall of these disciplines use quantitative methods and analysis techniques to answer different questions related to drug research, development, and marketing. By bringing theory and applications of these disciplines together in one book, we hope to allow the reader to learn more about different quantitative fields and recognize similarities and differences in theory and applications employed by different disciplines. This book is aimed at people interested in quantitative methods and applications used in the pharmaceutical industry, experts working in these areas, and students looking for applications and career options in quantitative sciences.

Each chapter of this book is self-contained and written by different authors. Chapter , readers are introduced to common biostatistical methods used in the analysis and interpretation of pharmacoepidemiological data. This chapter also briefly describes how to take into account common issues in observational epidemiology, such as bias, confounding, and interactions, in order to establish a clear causal link between exposure and drug effect.

Chapter provides case studies illustrating the impact of collaboration between biostatisticians, pharmacometricians, clinicians, formulation, and laboratory scientists. It also explains how working as a team and using quantitative modeling and simulation methodologies can result in significant efficiencies and improvements in the drug development process.

This book is a collaborative effort from several authors and based on knowledge and experience gained from working in academia, the pharmaceutical industry, and regulatory agencies. We present material that hopefully will be interesting to a broad and diverse audience. The views expressed in this book are those of the authors and do not necessarily represent the views of organizations with which the authors have been or are presently affiliated.

We would like to acknowledge and thank all authors who contributed to this book and reviewers who helped us review and improve the different chapters. We appreciate the constructive comments provided by Jos Pinheiro, Michael Hale, Ken Chase, Tony Zagar, Seth Berry, Ilya Lipkovich, and Russell Reeve. From our employers, we thank Torsten Westermeier, Bayer Statistics and Data Insights, and Lisa DiPippo, the University of Rhode Island Department of Computer Science and Statistics, for the encouragement. Additionally, we thank the Springer Publishing Agency for giving us an opportunity to publish this book and the Editor of Mathematics and Statistics, Springer US Christopher Tominich for his patience and helpful tips. Finally, we thank our special friends, Mikhail Benediktovich and Maria Francevna, and our families for encouragement and support.