Applied Statistics in the Pharmaceutical Industry: With Case Studies Using S-Plus PDF
Preview Applied Statistics in the Pharmaceutical Industry: With Case Studies Using S-Plus
Applied Statistics in the Pharmaceutical Industry Springer Science+Business Media, LLC Steven P. Millard Andreas Krause Editors Applied Statistics in the Pharmaceutical Industry With Case Studies Using S-PLUS With 131 Illustrations " Springer Steven P. Millard Andreas Krause Probability, Statistics & Information Novartis Pharma AG 7723 44th Avenue NE Biostatistics Seattle, WA 98115-5117 P.O. Box USA 4002 Basel Switzerland Library of Congress Cataloging-in-Publication Data Applied statistics in the pharmaceutical industry : with case studies using S-PLus I editors, Steven P. Millard, Andreas Krause. p. cm. Includes bibliographical references and index. ISBN 978-1-4419-3166-5 ISBN 978-1-4757-3466-9 (eBook) DOI 10.1007/978-1-4757-3466-9 1. Drugs-Research-Statistical methods. I. MilIard, Steven P. II. Krause, Andreas. RS122 .A66S 2001 615'. 19'0727---dc21 00-053767 Printed on acid-free paper. S-PLus is a registered trademark of Insightful Corporation. @ 2001 Springer Science+Business Media New York Originally published by Springer-Verlag New Y orle, Inc. in 2001 Softcover reprint ofthe hardcover Ist edition 2001 A1l rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieva1, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter deve10ped is forbidden. The use of general descriptive names, trade names, trademarks, etc., in this publication, even if The former are not especially identified, is not to be taken as a sign that such names, as understood By the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone. Production managed by Timothy Taylor; manufacturing supervised by Jerome Basma. Camera-ready copy prepared from the authors' JnEX2e and Microsoft Word files. 9 8 7 6 5 4 3 2 1 ISBN 978-1-4419-3166-5 SPIN 10716954 Preface Each year, hundreds of new drugs are approved for the marketplace. The ap proval of a single new drug is the result of years of screening tens of thousands of compounds, performing preclinical research on their effects, and designing, implementing, and analyzing the results of clinical trials. Statisticians are in volved in every phase of this process. Between the years 1960 and 2000, the number of statisticians working in the pharmaceutical industry grew from less than 100 to over 2500. This book provides a general (but not exhaustive) guide to statistical meth ods used in the pharmaceutical industry, and illustrates how to use S-PLUS to implement these methods. Specifically, each chapter in this book: • Illustrates statistical applications in the pharmaceutical industry. • Illustrates how the statistical applications can be carried out using S-PLUS (each chapter except the first contains an appendix with S-PLUS code). • Illustrates why S-PLUS is a useful software package for carrying out these applications. • Discusses the results and implications of a particular application. The target audience for this book is very broad, including: • Graduate students in biostatistics. • Statisticians who are involved in the industry as research scientists, regulators, academics, and/or consultants who want to know more about how to use S-PLUS and learn about other subfields within the industry that they may not be familiar with. • Statisticians in other fields who want to know more about statisti- cal applications in the pharmaceutical industry. The data and code from each chapter are available on the following web site: http://www2.active.ch/-krause.aldoc/statistics-in-pharmal. (Due to confidenti ality, the raw data are not available for some chapters.) Part 1 of this book includes Chapter 1, which is an introductory chapter ex plaining the history and current state of statistics in the drug development proc ess. Following Chapter 1, this book is divided into six more sections that follow the sequence of the drug development process (see Figure 1.2). Part 2 encom passes basic research and preclinical studies and Chapter 2 within this section discusses one-factor comparative studies. Part 3 covers preclinical safety as sessment. Chapter 3 discusses analysis of animal carcinogenicity data, and Chapter 4 toxicokinetic and pharmacokinetic data. Part 4 involves Phase I studies. Chapter 5 discusses the analysis of pharmacokinetic data in humans, Chapter 6 illustrates graphical presentations of single patient results, Chapter 7 explains the design and analysis of Phase I oncology trials, Chapter 8 discusses vi Preface the analysis of analgesic trials, Chapter 9 points out problems with patient com pliance and how this affects pharmacokinetic analysis, and Chapter 10 discusses the classic 2,2,2 crossover design. Part 5 includes chapters on Phase IT and ill clinical trials. Chapter 11 discusses power and sample size calculations, Chapter 12 compares how S-PLUS and SAS handle analysis of variance, Chapter 13 ex plains a technique for sample size reestimation, Chapter 14 discusses permuta tion tests for Phase ill clinical trials, Chapter 15 discusses comparing two treat ments in a Phase ill clinical trial, and Chapter 16 covers meta-analysis of clini cal trials. Part 6 covers Phase IV studies, and includes Chapter 17 on the analy sis of health economic data. Finally, Part 7 encompasses manufacturing and production. Chapter 18 discusses the decimal reduction time of a sterilization process, and Chapter 19 covers acceptance sampling plans by attributes. A Note about S-PLUS Versions The chapters in this book were written when the current versions of S-PLUS were Version 3.4 and then 5.1 for UNIX, and Version 4.5 and then 2000 for Windows. The Windows version of S-PWS includes a graphical user interface (GUI) with pull-down menus and buttons. By the time this book is published, both S-PWS 6.0 for UNIX and S-PWS 6.0 for Windows will be available, and both of these versions of S-PWS will have GUIs. All of the chapters in this book, however, illustrate how to use S-PLUS by writing S-PLUS commands and functions, rather than using pull-down menus. Typographic Conventions Throughout this book, S-PWS commands and functions are displayed in a ftxed width font, for example: summary (aov .psize). Within chapters, S-PLUS commands are preceded with> (the "greater than" sign), which is the default S-PWS prompt. Rather than use the S-PWS continuation prompt (+ by default) for continuation lines, we follow Venables and Ripley (1999) and use indenta tion instead. Within appendices, S-PWS prompts are omitted. Companion Web Site for This Book For more information about the authors, datasets, software, and related links, please refer to http://www2.active.ch/-krause.aldoc/statistics-in-pharmal. Information on S-PLUS For infonnation on S-PLUS, please contact Insightful Corporation: Insightful Corporation 1700 Westlake Ave N, Suite 500 Seattle, W A 98lO9-3044 USA 800-569-0123 [email protected] www.insightful.com Insightful Corporation Knightway House Park Street Bagshot, Surrey GU195AQ United Kingdom +44 1276 452 299 [email protected] www.uk.insightful.com Acknowledgments In the Spring of 1997, one of us (Steve) taught a course on using S-PLUS at two different branches of Merck Research Laboratories. The course was organized by Charles Liss and Thomas Bradstreet of Merck, and Charles (Chuck) Taylor of MathSoft, Inc. (now Insightful Corporation), and revolved around using S-PLUS to analyze three different data sets from the pharmaceutical industry. That course was the seed for this book. Thomas Bradstreet put Steve in touch with several statisticians and provided early guidance on the scope and direction of this book. Andreas, who works in the pharmaceutical industry, had already published a book with Melvin Olson on using S-PLUS (The Basics of Sand S-PLUS), and therefore knew several people who became contributors to this book. Weare grateful to all of the authors who generously donated their time and efforts to writing chapters for this book. We would also like to thank John Kimmel for his help and guidance throughout the process of editing this book. Steve Millard would like to thank Aleksandr Aravkin, Sam Coskey, and Jeannine Silkey for their help in formatting many of the chapters. Finally, we would like to thank all of the statisticians in the phar maceutical industry; you have devoted yourselves to improving the health of the world.
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