Pharmaceutical Statistics Using SAS

Alex Dmitrienko Ph.D., Christy Chuang-Stein Ph.D., Ralph B. D'Agostino Sr. Ph.D.

Description

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.

Sujets

• COMPUTERS
• Clinical research
• Drug development
• Enterprise software
• Clinical trial
• General officer
• Biostatistics
• General

Informations

• Publié par: SAS Institute
• Date de parution: 07 février 2007
• Nombre de lectures: 0
• EAN13: 9781629590301
• Langue: English
• Poids de l'ouvrage: 3 Mo

Extrait

Praise from the Experts


“Pharmaceutical Statistics Using SAS contains applications of cutting-edge statistical techniques using
cuttingedge software tools provided by SAS. The theory is presented in down-to-earth ways, with copious examples,
for simple understanding. For pharmaceutical statisticians, connections with appropriate guidance documents
are made; the connections between the document and the data analysis techniques make ‘standard practice’
easy to implement. In addition, the included references make it easy to find these guidance documents that are
often obscure.
“Specialized procedures, such as easy calculation of the power of nonparametric and survival analysis tests, are
made transparent, and this should be a delight to the statistician working in the pharmaceutical industry, who
typically spends long hours on such calculations. However, non-pharmaceutical statisticians and scientists will
also appreciate the treatment of problems that are more generally common, such as how to handle dropouts and
missing values, assessing reliability and validity of psychometric scales, and decision theory in experimental
design. I heartily recommend this book to all.”
Peter H. Westfall
Professor of Statistics
Texas Tech University


“The book is well written by people well known in the pharmaceutical industry. The selected topics are
comprehensive and relevant. Explanations of the statistical theory are concise, and the solutions are up-to-date.
It would be particularly useful for isolated statisticians who work for companies without senior colleagues.”

Frank Shen
Executive Director
Global Biometric Sciences
Bristol-Myers Squibb Co.


“This book covers an impressive range of topics in clinical and non-clinical statistics. Adding the fact that all
the datasets and SAS code discussed in the book are available on the SAS Web site, this book will be a very
useful resource for statisticians in the pharmaceutical industry.”

Professor Byron Jones
Senior Director
Pfizer Global Research and
Development, UK
“The first thing that catches one’s attention about this very interesting book is its breadth of coverage of
statistical methods applied to pharmaceutical drug development. Starting with drug discovery, moving
through pre-clinical and non-clinical applications, and concluding with many relevant topics in clinical
development, the book provides a comprehensive reference to practitioners involved in, or just interested to
learn about, any stage of drug development.

“There is a good balance between well-established and novel material, making the book attractive to both
newcomers to the field and experienced pharmaceutical statisticians. The inclusion of examples from real
studies, with SAS code implementing the corresponding methods, in every chapter but the introduction, is
particularly useful to those interested in applying the methods in practice, and who certainly will be the
majority of the readers. Overall, an excellent addition to the SAS Press collection.”

José Pinheiro
Director of Biostatistics
Novartis Pharmaceuticals


“This is a very well-written, state-of-the-art book that covers a wide range of statistical issues through all
phases of drug development. It represents a well-organized and thorough exploration of many of the important
aspects of statistics as used in the pharmaceutical industry. The book is packed with useful examples and
worked exercises using SAS. The underlying statistical methodology that justifies the methods used is clearly
presented.

“The authors are clearly expert and have done an excellent job of linking the various statistical applications to
research problems in the pharmaceutical industry. Many areas are covered including model building,
nonparametric methods, pharmacokinetic analysis, sample size estimation, dose-ranging studies, and decision
analysis. This book should serve as an excellent resource for statisticians and scientists engaged in
pharmaceutical research or anyone who wishes to learn about the role of the statistician in the pharmaceutical
industry.”
Barry R. Davis
Professor of Biomathematics
University of Texas
Pharmaceutical Statistics
®Using SAS
A Practical Guide
Edited by
Alex Dmitrienko
Christy Chuang-Stein
Ralph D’Agostino
The correct bibliographic citation for this manual is as follows: Dmitrienko, Alex, Christy Chuang-Stein, and Ralph
®D’Agostino. 2007. Pharmaceutical Statistics Using SAS : A Practical Guide. Cary, NC: SAS Institute Inc.
®Pharmaceutical Statistics Using SAS : A Practical Guide
Copyright © 2007, SAS Institute Inc., Cary, NC, USA
ISBN: 978-1-59047-886-8
All rights reserved. Produced in the United States of America.
For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in
any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission
of the publisher, SAS Institute Inc.
For a Web download or e-book: Your use of this publication shall be governed by the terms established by the
vendor at the time you acquire this publication.
U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related
documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in
FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).
SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513.
1st printing, February 2007
®
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®
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Contents
1 Statistics in Drug Development 1
By Christy Chuang-Stein and Ralph D’Agostino
1.1 Introduction 1
1.2 Statistical Support to Non-Clinical Activities 2
1.3 S Support to Clinical Testing 3
1.4 Battling a High Phase III Failure Rate 4
1.5 Do Statisticians Count? 5
1.6 Emerging Opportunities 5
1.7 Summary 6
References 6
2 Modern Classification Methods for Drug Discovery 7
By Kjell Johnson and William Rayens
2.1 Introduction 7
2.2 Motivating Example 9
2.3 Boosting 10
2.4 Model Building 27
2.5 Partial Least Squares for Discrimination 33
2.6 Summary 42
References 42
3 Model Building Techniques in Drug Discovery 45
By Kimberly Crimin and Thomas Vidmar
3.1 Introduction 45
3.2 Example: Solubility Data 46
3.3 Training and Test Set Selection 47
3.4 Variable Selection 51
3.5 Statistical Procedures for Model Building 58
3.6 Determining When a New Observation Is Not in a Training Set 61
3.7 Using SAS Enterprise Miner 63
3.8 Summary 67
References 67iv Pharmaceutical Statistics Using SAS: A Practical Guide
4 Statistical Considerations in Analytical Method Validation 69
By Bruno Boulanger, Viswanath Devanaryan, Walth`ere Dew´e,
and Wendell Smith
4.1 Introduction 69
4.2 Validation Criteria 73
4.3 Response Function or Calibration Curve 74
4.4 Linearity 83
4.5 Accuracy and Precision 85
4.6 Decision Rule 88
4.7 Limits of Quantification and Range of the Assay 92
4.8 Limit of Detection 93
4.9 Summary 93
4.10 Terminology 94
References 94
5 Some Statistical Considerations in Nonclinical Safety Assessment 97
By Wherly Hoffman, Cindy Lee, Alan Chiang, Kevin Guo, and Daniel Ness
5.1 Overview of Nonclinical Safety Assessment 97
5.2 Key Statistical Aspects of Toxicology Studies 98
5.3 Randomization in Toxicology Studies 99
5.4 Power Evaluation in a Two-Factor Model for QT Interval 102
5.5 Statistical Analysis of a One-Factor Design with Repeated Measures 106
5.6 Summary 113
Acknowledgments 115
References 115
6 Nonparametric Methods in Pharmaceutical Statistics 117
By Paul Juneau
6.1 Introduction 117
6.2 Two Independent Samples Setting 118
6.3 The One-Way Layout 129
6.4 Power Determination in a Purely Nonparametric Sense 144
Acknowledgments 149
References 149
7 Optimal Design of Experiments in Pharmaceutical Applications 151
By Valerii Fedorov, Robert Gagnon, Sergei Leonov, and Yuehui Wu
7.1 Optimal Design Problem 152
7.2 Quantal Dose-Response Models 159
7.3 Nonlinear Regression Models with a Continuous Response 165
7.4 Regression Models with Unknown Parameters in the Variance Function 169
7.5 Models with a Bounded Response (Beta Models) 172
7.6 Models with a Response (Logit Link) 176
7.7 Bivariate Probit Models for Correlated Binary Responses 181
7.8 Pharmacokinetic Models with Multiple Measurements per Patient 184