Introduction

The past decades have witnessed significant developments of biostatistical methodology applied to all areas of pharmaceutical drug development. These applications range from drug discovery to animal studies to Phase III trials. The use of statistical methods helps optimize a variety of drug development processes and ultimately helps ensure that new chemical entities are pure and stable, new therapies are safe and efficacious.

For the most part, new developments in biostatistical theory and applications are scattered across numerous research papers or books on specialized topics and rarely appear under the same cover. The objective of this book is to offer a broad coverage of biostatistical methodology used in drug development and practical problems facing today's drug developers.

Each chapter of the book features a discussion of methodological issues, traditional and recently developed approaches to data analysis, practical advice from subject matter experts, and review of relevant regulatory guidelines. The book is aimed at practitioners and therefore does not place much emphasis on technical details. It shows how to implement the algorithms presented in the various chapters by using built-in SAS procedures or custom SAS macros written by the authors. In order to help readers better understand the underlying concepts and facilitate application of the introduced biostatistical methods, the methods are illustrated with a large number of case studies from actual pre-clinical experiments and clinical trials. Since many of the statistical issues encountered during late-phase drug development (e.g., survival analysis and interim analyses) have been covered in other books, this book focuses on statistical methods to support research and early drug development activities.

Although the book is written primarily for biostatisticians, it will benefit a broad group of pharmaceutical researchers, including biologists, chemists, pharmacokineticists and pharmacologists. Most chapters are self-contained and include a fair amount of high-level introductory material. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students.

We hope that this book will help close the gap between modern statistical theory and existing data analysis practices in the pharmaceutical industry. By doing so, we hope the book will help advance drug discovery and development.