Table Of ContentApplied 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.
