Analysis of Clinical Trials Using SAS- A. Dmitrienko, et al., (SAS, 2005) WW

430 Pages·2005·6.34 MB·English

Praise From the Experts

“For the nonstatistician, the array of statistical issues in design and analysis of clinical trials can be
overwhelming. Drawing on their years of experience dealing with data analysis plans and the
regulatory environment, the authors of Analysis of Clinical Trials Using SAS: A Practical Guide have
done a great job in organizing the statistical issues one needs to consider both in the design phase and
in the analysis and reporting phase. As the authors demonstrate, SAS provides excellent tools for
designing, analyzing and reporting results of comparative trials, making this book very attractive for
the nonstatistician.

This book will also be very useful for statisticians who wish to learn the most current statistical
methodologies in clinical trials. The authors make use of recent developments in SAS - including
stratification, multiple imputation, mixed models, nonparametrics, and multiple comparisons
procedures - to provide cutting-edge tools that are either difficult to find or unavailable in other
software packages. Statisticians will also appreciate the fact that sufficient technical details are
provided.

Because clinical trials are so highly regulated, some of the most rigorous and highly respected
statistical tools are used in this arena. The methodologies covered in this book have applicability to the
design and analysis of experiments well beyond clinical trials; researchers in all fields who carry out
comparative studies would do well to have it on their bookshelves.”

Peter Westfall, Texas Tech University

“This is a very impressive book. Although it doesn't pretend to cover all types of analyses used in
clinical trials, it does provide unique coverage of five extremely important areas. Each chapter
combines a detailed literature review and theoretical discussion with a useful how-to guide for
practitioners. This will be a valuable book for clinical biostatisticians in the pharmaceutical industry.”

Steve Snapinn, Amgen

“Chapter 1 (“Analysis of Stratified Data”) will make a very fine and useful addition to the literature on
analyzing clinical trials using SAS.”

Stephen Senn, University of Glasgow

“Chapter 2 ("Multiple Comparisons and Multiple Endpoints") provides an excellent single-source
overview of the statistical methods available to address the critically important aspect of multiplicity
in clinical trial evaluation. The treatment is comprehensive, and the authors compare and contrast
methods in specific applications. Newly developed gatekeeping methods are included, and the
available software makes the approach accessible. The graphical displays are particularly useful in
understanding the concept of adjusted p-values.”

Joe Heyse, Merck

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The correct bibliographic citation for this manual is as follows: Dmitrienko, Alex, Geert Molenberghs,
Christy Chuang-Stein, and Walter Offen. 2005. Analysis of Clinical Trials Using SAS®: A Practical Guide.
Cary, NC: SAS Institute Inc.

Analysis of Clinical Trials Using SAS
®: A Practical Guide

Copyright © 2005, SAS Institute Inc., Cary, NC, USA

ISBN 1-59047-504-6

All rights reserved. Produced in the United States of America.

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.

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 2005

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Contents

1
Analysis of Stratified Data
1
1.1
Introduction
1
1.2
Continuous Endpoints
3
1.3
Categorical Endpoints
19
1.4
Time-to-Event Endpoints
40
1.5
Tests for Qualitative Interactions
56

2
Multiple Comparisons and Multiple Endpoints
67
2.1
Introduction
67
2.2
Single-Step Tests
70
2.3
Closed Testing Methods
74
2.4
Fixed-Sequence Testing Methods
84
2.5
Resampling-Based Testing Methods
89
2.6
Testing Procedures for Multiple Endpoints
94
2.7
Gatekeeping Strategies
104

3
Analysis of Safety and Diagnostic Data
129
3.1
Introduction
129
3.2
Reference Intervals for Safety and Diagnostic Measures
130
3.3
Analysis of Shift Tables
157

4
Interim Data Monitoring
177
4.1
Introduction
177
4.2
Repeated Significance Tests
181
4.3
Stochastic Curtailment Tests
235

5
Analysis of Incomplete Data
269
5.1
Introduction
269
5.2
Case Studies
271
5.3
Data Setting and Modeling Framework
273
5.4
Analysis of Complete Growth Data
276
5.5
Simple Methods and MCAR
287
5.6
Available Case Methods
300
5.7
Likelihood-Based Ignorable Analyses
301

iv
Analysis of Clinical Trials Using SAS: A Practical Guide

5.8
Multiple Imputation
308
5.9
The EM Algorithm
326
5.10
Categorical Data
332
5.11
MNAR and Sensitivity Analysis
353
5.12
Summary
354

Appendix
355

References
391

Index
405

Preface

Introduction
Clinical trials have long been one of the most important tools in the arsenal of clinicians and scientists
who help develop pharmaceuticals, biologics, and medical devices. It is reported that nearly 10,000
clinical studies are conducted every year around the world. One can find many excellent books that
address fundamental statistical and general scientific principles underlying the design and analysis of
clinical trials, such as those by Pocock (1983), Fleiss (1986), Meinert (1986), Friedman, Furberg and
DeMets (1996), Piantadosi (1997) and Senn (1997). Numerous references can be found in these fine
books.
The aim of this book is unique in that we focus in great detail on a set of selected and practical
problems facing statisticians and biomedical scientists conducting clinical research. We discuss
solutions to these problems based on modern statistical methods and review computer-intensive
techniques that help clinical researchers efficiently and rapidly implement these methods in the powerful
SAS environment.
It is a challenge to select the few topics that are most important and relevant to the design and
analysis of clinical trials. Our choice of topics for this book was guided by the International Conference
on Harmonization (ICH) guideline for the pharmaceutical industry entitled “Structure and Content of
Clinical Study Reports” (this document is commonly referred to as ICH E3). The document states that

Important features of the analysis, including the particular methods used, adjustments made for
demographic or baseline measurements or concomitant therapy, handling of dropouts and missing
data, adjustments for multiple comparisons, special analyses of multicenter studies, and
adjustments for interim analyses, should be discussed [in the study report].

Following the ICH recommendations, we decided to focus in this book on the analysis of stratified
data, incomplete data, multiple inferences, and issues arising in safety and efficacy monitoring. We also
address other statistical problems that are very important in a clinical trial setting, such as reference
intervals for safety and diagnostic measurements.
One special feature of the book is the inclusion of numerous SAS macros to help readers implement
the new methodology in the SAS environment. The availability of the programs and the detailed
discussion of the output from the macros help make the application of new procedures a reality. The
authors are planning to make the SAS macros compatible with new SAS products such as SAS
Enterprise Guide. Enterprise Guide tasks that implement the statistical methods discussed in the book
will be published on the SAS Enterprise Guide Users Group Web site at http://www.segus.org.
The book is aimed at clinical statisticians and other scientists who are involved in the design and
analysis of clinical trials conducted by the pharmaceutical industry, academic institutions, or
governmental institutions such as the National Institutes of Health (NIH). Graduate students specializing
in biostatistics will also find the material in this book useful because of its applied nature.
Because the book is written for practitioners, it concentrates primarily on solutions rather than
theory. Although most of the chapters include some tutorial material, this book was not intended to

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Analysis of Clinical Trials Using SAS: A Practical Guide

provide a comprehensive coverage of the selected topics. Nevertheless, each chapter gives a high-level
description of the theoretical aspects of the statistical problem at hand and includes references to
publications that contain more advanced material. In addition, each chapter gives a detailed overview of
the underlying statistical principles.
There are some exceptions to the presentation of minimum theory in the book. For example, Chapter
5 discusses the analysis of incomplete data and covers comparatively complex statistical concepts such
as multiple imputation. Although the theoretical part is written at a higher statistical level, examples and
applications are prepared in such a way that they can be easily understood.
Examples from real trials are used throughout the book to illustrate the concepts being discussed and
to help the reader understand their relevance in a clinical trial setting. Most of the data come from real
clinical trials. In several cases, because of confidentiality concerns, we relied on simulated data that are
representative of real clinical trial data. Although simulated data might lack authenticity, using them
does afford us the opportunity to see how close to the truth we can get using the proposed methodology.
In this regard, we echo Hastie and Tibshirani’s (1990, page 239) statement that “an advantage of using
simulated examples is that you know the truth.”

Outline of the book
Chapter 1: Analysis of Stratified Data. This chapter discusses the analysis of clinical outcomes in the
presence of influential covariates. It reviews stratified analyses of continuous, categorical, and
time-to-event endpoints. The chapter also introduces statistical methods for studying
treatment-by-stratum interactions.
Chapter 2: Multiple Comparisons and Multiple Endpoints. This chapter reviews statistical
strategies for handling multiplicity issues arising in clinical trials. It covers basic single-step multiple
tests as well as more advanced closed, fixed-sequence and resampling-based multiple testing
procedures. The chapter also describes strategies for handling multiple objectives and dose-finding
studies.
Chapter 3: Analysis of Safety and Diagnostic Data. This chapter describes methods for constructing
reference intervals for safety and diagnostic measures with various clinical trials applications, such
as computation of reference limits based on sample quantiles and tolerance limits. The second part of
the chapter reviews statistical methods for the analysis of shift tables produced after reference
intervals have been applied to safety and diagnostic data.
Chapter 4: Interim Data Monitoring. The first part of this chapter reviews the popular approaches for
designing and monitoring group sequential trials, such as repeated significance tests and the error
spending function approach. The second part introduces stochastic curtailment methods, including
frequentist, mixed Bayesian-frequentist, and fully Bayesian methods.
Chapter 5: Analysis of Incomplete Data. This chapter discusses basic and more advanced statistical
methods for the analysis of incomplete longitudinal data. These methods include complete case
analysis, last observation carried forward, likelihood-based methods, and multiple imputation.

Acknowledgments
We would like to thank the following individuals (listed alphabetically) for their valuable comments:

Caroline Beunckens (Limburgs Universitair Centrum)
Prof. Raymond Carroll (Texas A&M University)
Dr. Brenda Gaydos (Lilly)
Dr. Jeffrey Helterbrand (Genentech)
Dr. Joseph Heyse (Merck)
Prof. Jason Hsu (Ohio State University)
Ivy Jansen (Limburgs Universitair Centrum)

Preface
vii

Prof. Michael Kenward (London School of Hygiene and Tropical Medicine)
Prof. Richard Kryscio (University of Kentucky)
Dr. Gordon Lan (Aventis)
Dr. Ilya Lipkovich (Lilly)
Dr. Nuwan Nanayakkara (Amylin)
Dr. Gerhardt Pohl (Lilly)
Dr. Andreas Sashegyi (Lilly)
Prof. Stephen Senn (Glasgow University)
Dr. Herbert Thijs (Limburgs Universitair Centrum)
Prof. Geert Verbeke (Katholieke Universiteit Leuven)
Prof. Peter Westfall (Texas Tech University)
Dr. Ilker Yalcin (Lilly)

We are grateful to Grace Ying Li (Lilly) who has carefully tested most of the SAS programs in this
book and helped us optimize some of the complicated SAS macros.
Alex Dmitrienko and Walt Offen would like to sincerely thank Lilly Global Statistical Science senior
management, in particular Drs. Carlos Alatorre, Todd Sanger and Mike Wilson, for their enthusiastic
and continual support of this endeavor.
Geert Molenberghs would like to thank his colleagues for their support.
Christy Chuang-Stein wishes to thank Drs. Mohan Beltangady and Debra Gmerek for their support
and encouragement.
We also thank Donna Faircloth, our editor at SAS Books by Users Press, for her support and
assistance in preparing this book.