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Paperback. Zustand: Good. Pharmaceutical Statistics Using SAS: A Practical Guide offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these: drug discovery experiments to identify promising chemical compounds animal studies to assess the toxicological profile of these compounds clinical pharmacology studies to examine the properties of new drugs in healthy human subjects Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs.Additional features include a discussion of methodological issues, practical advice from subject-matter experts, and review of relevant regulatory guidelines. Most chapters are self-contained and include a fair amount of high-level introductory material to make them accessible to a broad audience of pharmaceutical scientists. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students. This book is part of the SAS Press program.…

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Paperback. Zustand: Good. Pharmaceutical Statistics Using SAS: A Practical Guide offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these: drug discovery experiments to identify promising chemical compounds animal studies to assess the toxicological profile of these compounds clinical pharmacology studies to examine the properties of new drugs in healthy human subjects Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs.Additional features include a discussion of methodological issues, practical advice from subject-matter experts, and review of relevant regulatory guidelines. Most chapters are self-contained and include a fair amount of high-level introductory material to make them accessible to a broad audience of pharmaceutical scientists. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students. This book is part of the SAS Press program.…

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Soft cover. Zustand: Very Good. 1st Edition. Large format paperback, ix + 444 pages, NOT ex-library. Weight over 1.3kg. Light wear only, faint handling marks on outer page edges. Interior is clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. Uncreased spine. -- This comprehensive guide revolutionizes the application of statistical methodologies in pharmaceutical drug development, bridging the chasm between cutting-edge biostatistical theory and industry practices. Authored by renowned experts in the field, Pharmaceutical Statistics Using SAS delivers a practical, case-study-driven approach to navigating the labyrinthine world of drug discovery, preclinical trials, and clinical studies. Spanning 14 chapters, the book demystifies complex statistical techniques - from modern classification methods in drug discovery and optimal experimental design to survival analysis in clinical trials - through real-world examples, step-by-step SAS implementations, and regulatory insights. Unlike theoretical texts, this volume empowers practitioners - biostatisticians, pharmacologists, regulatory scientists - with actionable tools to tackle pressing challenges: validating analytical methods, assessing dose-response trends, handling missing data, and applying decision theory for Go/No-Go decisions. By harmonizing innovative statistical frameworks with industry-grade SAS coding, the authors democratize advanced analytics, enabling even novice statisticians to confidently drive drug development processes. This is not merely a textbook; it's a survival kit for today's data-driven pharmaceutical landscape, ensuring faster discoveries, safer therapies, and smarter regulatory strategies. Whether you're a seasoned biostatistician or an aspiring drug developer, this book transforms statistical theory into tangible solutions for real-world drug development bottlenecks.…

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