Recent Advances in Clinical Trial Design and Analysis

262 Pages·1995·14.547 MB·English

by  David L. DeMets, Gordon Lan (auth.), Peter F. Thall Ph.D. (eds.)

Recent Advances in Clinical Trial Design and Analysis
Cancer Treatment and Research
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Recent Advances in Clinical
Trial Design and Analysis
edited by
Peter F. ThaII, Ph.D.
Deparlmenl oI Biomalhematics
The UniversilY oI Texas M. D. Andersoll Cancer Center
Haus/on, Texas
••
SPRINGER SCIENCE+BUS1NESS MEDIA, LLC , ..
Library of Congress Cataloging-in-Publication Data
Recent advances in clinical trial design and analysis / edited by
Peter F. Thall.
p. cm. - (Cancer treatment and research; CTAR 75)
Includes bibliographical references and index.
ISBN 978-1-4613-5830-5 ISBN 978-1-4615-2009-2 (eBook)
DOI 10.1007/978-1-4615-2009-2
1. Clinical trials. 2. Medicine - Research - Statistical methods.
I. Thall, Peter F. II. Series: Cancer treatment and research; v. 75.
[DNLM: 1. Clinical Trials - methods. 2. Research Design.
3. Statistics. Wl CA693 v. 75 1995/ W 20.5 R295 1995]
R853.C55R426 1995
619-dc20
DNLM/DLC
for Library of Congress 94-41111
CIP
Copyright
© 1995 Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1995
Softcover reprint ofthe hardcover Ist edition 1995
All rights reserved. No part of this publication may be reproduced, stored in
a retrieval system or transmitted in any form or by any means, mechanical,
photo-copying, recording, or otherwise, without the prior written permission
of the publisher, Springer Science+Business Media, LLC.
Printed on acid-free paper.
Contents
ListofContributors. .......................................... vii
Preface. ................................... .................. ix
1. The alpha spendingfunction approach to interim
data analyses. ........................................... 1
DAVID L. DEMETS and GORDON LAN
2. Issues in the design and analysis ofAIDS clinical trials. ....... 29
DENNISO. DIXON and JEFFREY M. ALBERT
3. Recent developments in the design ofphase II clinical trials. ... 49
PETERF. THALLand RICHARD M. SIMON
4. Multivariate failure time data. ............................. 73
D.Y. LIN
5. Goodness-of-fit and diagnostics for proportional hazards
regression models. ....................................... 95
PATRICIAM. GRAMBSCH
6. A review oftree-based prognostic models ................... 113
MICHAEL LEBLANCand JOHN CROWLEY
7. Decision analysis and Bayesian methods in clinical trials. ...... 125
DONALD A. BERRY
8. A Bayesian model for evaluating specificity oftreatment effects
in clinical trials .......................................... 155
RICHARD M. SIMON, DENNIS O. DIXON, and BORIS FREIDLIN
9. The exact analysis ofcontingencytables in medical research 177
CYRUS R. MEHTA
10. Stratified-adjusted versus unstratified assessment of sample size
and power for analyses ofproportions ...................... 203
JOHN M. LACHIN and OLIVER M. BAUTISTA
11. Quality-of-Iife assessment in clinical trials 225
RICHARD D. GELBER and SHARI GELBER
Index. ...................................................... 247
VI
List of Contributors
Jeffrey M. Albert, National Institutes of Health, Solar Building, Room
2B26, Bethesda, MD 20892, (for overnight: 6003 Executive Boulevard,
Room 2B21, Rockville, MD 20852)
Oliver M. Bautista, The Biostatistics Center, Department of Statistics/
Statistical Computing, The George Washington University, 6110
Executive Boulevard, Rockville, MD 20852
Donald A. Berry, Institute of Statistics and Decision Sciences, and Cancer
Center Biostatistics, P.O. Box 90251, Duke University, Durham,
NC 27706
John Crowley, Fred Hutchinson CancerResearch CenterSeattle, WA 98104
DavidL.DeMets,DepartmentofStatisticsUniversityofWisconsin- Madison,
1210 W. Dayton Street, Madison, WI 53706
Dennis O. Dixon, National Institutes ofHealth, Solar Building, Room 2B21,
Bethesda, MD 20892, (for overnight: 6003 Executive Boulevard, Room
2B21, Rockville, MD 20852)
Boris Freidlin, Emmes Corporation, 11325 Seven Locks Road, Potomac,
MD 20854
Richard D. Gelber, Division of Biostatistics, Dana Faber Cancer Institute,
44 Binney Street, Boston, MA 02155
Shari Gelber, Division of Biostatistics, Dana Faber Cancer Institute, 44
Binney Street, Boston, MA 02155
Patricia M. Grambsch, Division of Biostatistics, School of Public Health,
University of Minnesota, A-460 Mayo Bldg., Box 197, 420 Delaware
Street SE, Minneapolis, MN 55455
John M. Lachin,The BiostatisticsCenter, DepartmentofStatistics/Statistical
Computing, The George Washington University, 6110 Executive
Boulevard, Suite 750, Rockville, MD 20852
Gordon Lan, The Biostatistics Center, Department of Statistics/Statistical
Computing, The George Washington University, 6110 Executive
Boulevard, Suite 750, Rockville, MD 20852
Michael LeBlanc, Department of Preventive Medicine and Biostatistics,
University ofToronto Toronto, M5S 1A8 Ontario, CANADA
D.Y. Lin, Department of Biostatistics, SC-32, University of Washington,
Seattle, WA 98195
vii
Cyrus R. Mehta, Department of Biostatistics, Harvard School of Public
Health and, Cytel Software Corporation, 675 Massachusetts Avenue,
Cambridge, MA 02139
Richard M. Simon, Chief, Biometrics Research Branch, Cancer Therapy
Evaluation Program (CTEP), National Cancer Institute (NCI), EPN
Room 739, Bethesda, MD 20892
Peter F. Thall, Department of Biomathematics, The University of Texas
M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Box 237,
Houston, TX 77030
viii
Preface
In early 1993, E.J. Freireich, M.D., asked me to edit a book on statistical
methods in clinical trials. Initially, I felt my job would be to assemble a
group of biostatisticians, each willing to contribute a chapter to the book.
The goal was to provide descriptions of some of the more modern develop
ments in statistical methods for clinical trials, for both medical and bio
statistical readers. Dr. Freireich's request presented a challenge to me,
because the proposed book was to appear in a series that previously had
consisted entirely of medical papers directed at a medical audience. I chose
biostatisticianscurrently doing cutting-edge research and asked each to write
a survey, possibly including new research, with a combined medical-bio
statistical audience in mind. The collection of chapters in this book is
the result.
Scientists tend to communicate within their own areas of expertise,
typically using a specialized language that often seems incomprehensible to
those outside their field. The problem of interdiscipline communication
communicatingideas between distinctgroupsofpeoplewho think differently
and use different languages - is one of the most difficult and important
problems in science. When one of the fields is statistics, the problem can be
even more difficult and challenging than usual. It is more difficult because
statistical concepts have their foundation in mathematics, in particular
probability, and translating mathematical concepts into language under
standable by nonmathematicians is, in my experience, one of the most
intellectually demanding activities that can be undertaken.
Communicating statistical concepts to biomedical scientists who have had
little or no statistical training is critically important, however. Clinical trials
have two purposes - to treat the patients in the trial, and to obtain
information that increasesour understanding ofthe disease and especially of
how patients respond to treatment. Statistical design provides a means to
achieve both these aims, while statistical data analysis provides methods for
extracting useful information from the trial data.
Recent advances in statistical computing, both in the computers them
selves and in conceptual devices such as computational algorithms, have
enabled statisticians to implement very rapidly a broad array of methods
IX
that previously were either impractical or impossible. Biostatisticians thus
have become able to provide much greater support to medical researchers
working in both clinical and laboratory settings. As our collective toolkit of
techniques for analyzing data and designing clinical trials and laboratory
studies has grown, however, it has become increasingly difficult for each of
us biostatisticians to keep up with all the developmentsin ourown field. The
task of communicating these advances to our medical colleagues thus has
become doubly difficult just as we are entering a new age in which so many
truly powerful statistical methods are becoming practical realities.
This book is one attempt, among many currently ongoing, to explain
some of the more recent developments in biostatistics to clinicians and
scientists who work in clinical trials. Each of the chapters describes a very
recent development in statistical methodology that is a powerful tool for
planning or analysis. The chapters are written at avariety oftechnical levels,
reflecting the individual stylesofthe contributors. Thus, I think unavoidably,
some chapters will be more accessible than others. If these chapters make
you aware of even one new method that you find useful, however, then I
have achieved my goal.
Peter F. Thall
x