Statistics in Medicine

710 Pages·2012·15.738 MB·738\710

Statistics in Medicine
Statistics in Medicine
Robert H. Riffenburgh
Statistics in Medicine
Third Edition
R. H. Riffenburgh
Clinical Investigation Department
Naval Medical Center San Diego
San Diego, California, USA
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First edition 1999
Second edition 2006
Third edition 2012
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12 13 14 15 10 9 8 7 6 5 4 3 2 1
This book is dedicated to
Gerrye, my love and best friend for sixty years;
Robin and Rick;
Scott, Alex, and Jasmine;
the memory of Marc;
Karen, Matt, Bryce, Aaron, and Sophia;
and Doug, Tricia, Nichole, and Ryan.
When told, “I’m too busy treating patients to do research,” I answer:
When you treat a patient, you treat 1 patient.
When you do research, you treat 10,000 patients.
Foreword to the
Third Edition
The Third Edition of Statistics in Medicine provides a wonderful pathway for peo-
ple to learn those methods (about 30% according to the author) used in 90% of
all statistical analyses. The message is clear: most statistical applications are con- xvii
ducted with a relatively few procedures and these are all covered in this book.
The first chapters cover planning studies and analysis, some fundamen-
tal definitions of probability and distributions. Many techniques are covered
including confidence intervals, hypothesis testing, categorical data, and epi-
demiologic methods. There is also material on managing results of analysis,
questionnaires and surveys, survival analysis, and logistic regression. Fifteen
databases are used in the book and more are available to both students and
professors on the Elsevier website.
Statistics in Medicine covers many basic statistical techniques and provides a
good starting point for further queries. In particular, the advice to meet a statis-
tician and be able to ask questions is invaluable.
The most important role of a statistician is to plan the study including the
design and the appropriate analysis. This is idiosyncratic, and not all statisti-
cians would agree. The statistician must distinguish and explain to the investi-
gator the difference between an observational study and an experiment. Most
observational studies do not get confirmed in experiments (clinical trials) so
it is wise to be modest in one’s claims about such studies. Observational stud-
ies offer important clues to further studies, generate hypotheses, but need con-
firmation by clinical trials. Unfortunately the media trumpet these studies as
if they have shown proof of a question. They cite a number of examples: cof-
fee causes pancreatic cancer, type A personality causes heart attacks, women
who eat cereal give birth to more boys… To protect oneself, I suggest they, the
reader, (1) ask if it makes sense biologically, (2) read this book.
Most statistics courses and programs only briefly discuss study conduct.
Importantly, this book considers data integrity, sample size determination,
maintaining control of missing values, and testing the pre-specified hypothe-
ses. In all analysis reports, it is important to list the data file being used, and
the commands for each analysis. In this way, one can reproduce the analysis
later. This is even more important when the set of commands involves editing
the data set. Have a research log in which you make notes (date them!) and can
refer to them later.
A major statistical procedure is multiple regression. There are many subtleties
here. One of the most obvious to statisticians and murky to others is the fact
xviii Foreword to the Third Edition
that statistical programs delete missing values from analyses. This leads to a
loss of sample size and possible bias if the missing values are related to the
outcome. Many methods have been proposed to handle this but none are fully
satisfactory. Recently, there have been new methods for validating the model.
Graphical methods may be used to give clues to the proper model, but they
are exploratory and not confirmatory, it is more difficult to do with multiple
covariables. These methods are useful for examining the assumptions in the
model. Stepwise methods have been available since at least 1960, but there are
serious problems with inference with these. Use with caution.
P. A. (Tony) Lachenbruch, PhD*
* Professor Lachenbruch is elected Fellow, American Statistical Association (ASA); past elected
Member, International Statistics Institute (ISI); past president, International Biometric Society
(IBS), Eastern North American Region (ENAR); past president, American Statistical Association;
past president IBS, Western North American Region (WNAR); former professor, Department of
Biostatistics, University of North Carolina; former Professor and Head, Biostatistics Department,
University of lowa; former Professor and Chairman, Biostatistics Department, University of
California Los Angeles (UCLA); former Director, Division of Biostatistics, Food and Drug
Administration (FDA); and currently Professor of Public Health, Oregon State University. Professor
Lachenbruch has published over 200 articles in statistical and medical journals and has served in
12 journal editorial positions.
Foreword to the
Second Edition
The Need for Clarity
To refer to the last half-century as revolutionary in the statistical world would
xix
be a gross understatement. I believe the effects of this revolution are more pro-
found in the world of medicine and other health-related sciences than in most
scientific disciplines. Most studies in health sciences today involve formal com-
parisons, requiring the use of appropriate statistical input on study design and
analysis. Some have become very sophisticated, as the FDA requires complex
and often large clinical trials, and most epidemiologic studies are more elabo-
rate than heretofore.
As a consequence, for those who aspire to engage in research activities, formal
training in statistics is a sine qua non. This is true even though most medical
research today is, by and large, collaborative, involving a team of scientists,
including at least one trained statistician. As with any endeavor, communica-
tion among team members is crucial to success and requires a common lan-
guage to bridge the gaps between their disciplines. Thus, although it is certainly
not necessary or expedient for most research scientists to become statisticians
or for most statisticians to become medical doctors, it is essential that they
develop these avenues of communication.
In fact, all health care practitioners who even occasionally glance at their
voluminous literature encounter statistical jargon, concepts, notation, and
reasoning. Of necessity, therefore, the training of most such practitioners
involves some exposure to statistics in order that clear communication and
understanding result.
Those statisticians who collaborate with medical researchers struggle mightily
with this problem of communicating with their colleagues. Often this involves
writing texts or reference books designed not to turn medical researchers into
statisticians but rather to train them in a common language in order to be
good consumers of our statistical products and good collaborators with their
statistical colleagues.
Providing the Tools
Dr. Riffenburgh addressed just this problem in the first edition of his success-
ful book on statistics in medicine. He presented statistics in language accessible
to health care practitioners so that they could effectively understand and com-
municate statistical information. To the second edition he brings new material,
all the while using the formula so successful in the first edition. He has added
xx Foreword to the Second Edition
three completely new chapters on multifactorial studies, equivalence test-
ing, and more uncommon tools. In addition, he has expanded several of the
original chapters with new sections on issues of study design, ethics, statistical
graphics, bias, and post hoc power. These additions, all presented in the same
spirit as in the first edition, make this an even more comprehensive resource.
This book is useful to medical researchers who have some training in statistics
and need only a reminder of some of the basic statistical tools and concepts.
Herein they will find, unencumbered by unnecessary formulas, equations,
symbols, or jargon, ready access to most of the fundamental tools useful
in medical research. Such access allows one to quickly find understandable
explanations of most of the material found in the literature, thereby enabling
understanding of the statistical analyses reported and facilitating a proper
interpretation of the results.
This book is also useful as a text. It is designed so that, in the hands of a
knowledgeable instructor, it can be used to provide students in most health-
related fields with the statistical background necessary for them to function as
researchers and/or practitioners in today’s increasingly quantitative world.
I congratulate Dr. Riffenburgh for his career as a medical statistician and for
this useful text/reference book, which I commend to all who teach statistics to
students in the health-related fields and to all who are engaged in or are con-
sumers of health-related research.
W.M. (Mike) O’Fallon, PhD*
* Dr. O’Fallon, Professor Emeritus of Biostatistics at the Mayo Clinic, has served as the Head of the
Division of Biostatistics and Chair of the Department of Health Science Research at Mayo. He was
president of the American Statistical Association in 2000 and served on its Board of Directors for 6
years. He has participated in hundreds of medical research endeavors and is a co-author on more
than 400 peer-reviewed manuscripts in medical literature. He chaired an NIH Study Section and
has served on an FDA advisory committee.