Converting Data into Evidence: A Statistics Primer for the Medical Practitioner

231 Pages·2013·2.503 MB·English

by  Alfred DeMaris, Steven H. Selman (auth.)

Alfred DeMaris
Steven H. Selman
Converting
Data into
Evidence
A Statistics Primer for the
Medical Practitioner
Converting Data into Evidence

Alfred DeMaris (cid:129) Steven H. Selman

Converting Data
into Evidence
A Statistics Primer for
the Medical Practitioner
Alfred DeMaris Steven H. Selman
Bowling Green State University Department of Urology
Bowling Green , OH , USA University of Toledo
Toledo , OH , USA
ISBN 978-1-4614-7791-4 ISBN 978-1-4614-7792-1 (eBook)
DOI 10.1007/978-1-4614-7792-1
Springer New York Heidelberg Dordrecht London
Library of Congress Control Number: 2013942308
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To Gabrielle and Linda

Preface
Let us go then, you and I ,
When the evening is spread out against the sky
Like a patient etherized upon a table ;
T. S. Elliott
Since the term was coined some 22 years ago (Guyatt 1991; Moayyedi 2008),
evidence-based medicine, or EBM, has taken center stage in the practice of medi-
cine. Adherence to EBM requires medical practitioners to keep abreast of the results
of medical research as reported in the general and specialty journals. At the heart of
this research is the science of statistics. It is through statistical techniques that
researchers are able to discern the patterns in the data that tell a clinical story worth
reporting. Like the astronomer’s telescope, statistics uncovers a universe that is
invisible to the naked eye. But if you are one of those souls for whom the statistical
machinations in the medical literature may as well be cuneiform script, this primer
is for you. In it, we invite the reader on a stroll through the landscape of statistical
science. We will, moreover, view that landscape while it is, in Elliott’s words,
“etherized upon a table”—anesthetized, inert, harmless.
This primer is intended for anyone who wishes to have a better grasp of the
meaning of statistical techniques as they are used in medical research. This includes
physicians, nurses, nurse practitioners, physician’s assistants, medical students,
residents, or even laypersons who enjoy reading research reports in medicine. The
book can also be useful for the physician engaged in medical research who is not
also a statistician. With the aid of this primer, that researcher will fi nd it easier to
communicate with the statisticians on his or her research team. Our intention is to
provide a background in statistics that allows readers to understand the application
of statistics in journal articles and other research reports in the medical fi eld. It is not
our intention to teach individuals how to perform statistical analyses of data or to be
statisticians. We leave that enterprise for the many more voluminous works in medi-
cal statistics that are out there. Rather the goal in this work is to provide a reader-
friendly introduction to the logic and the tools that underlie statistical science.
vii
viii Preface
In pursuit of this goal we have “cut to the chase” to a considerable degree.
We felt that it was important to limit attention to the aspects of statistics that the
reader was most likely to encounter on a routine basis. And we believed that it was
better to devote more space to a few important topics rather than try to inundate the
reader with too many different techniques. Thus, we have omitted extensive cover-
age of, say, the different ways of graphically displaying data. Other than examples
of graphs taken from the medical literature, there is no coverage of histograms,
stem-leaf plots, box plots, dot plots, or other such techniques. Similarly, we focus
on only the most basic summary measures of variable distributions and omit cover-
age of, say, the trimmed mean, the harmonic mean, the geometric mean, standard
scores, etc. Instead, we have dedicated more space to the subjects that we deem
most critical to an understanding of statistics as the discipline is practiced today:
causality and causal inference, internal and external validity of statistical results, the
sampling distribution of a statistic, the p value, common bivariate statistical proce-
dures, multivariable modeling and the meaning of statistical control, and measures
of the predictive effi cacy of statistical models, to cite a few examples.
Along with this approach, we have avoided the extensive presentation of statisti-
cal formulas and sophisticated mathematics. Anyone with even a passing grasp of
high-school algebra should have no trouble reading this primer. A few test-statistic
formulas are shown to communicate the rationale underlying test statistics. Other
than that, however, we simply name the tests that are used in different situations.
Some algebraic formulas, however, are unavoidable. It is simply not possible to
understand regression modeling in its different incarnations without showing regres-
sion equations. Similarly, growth-curve modeling and fi xed-effects regression mod-
eling are not understandable without their respective equations. Nevertheless, we
have tried to explain, in the narrative, what these equations are conveying in an
intuitive sense. And narrative is the operative word. This is not a traditional text-
book; there are no exercises and no tables in the back. To the extent that such could
be said about a statistics book, our intention was to make it a “good read.”
A feature of the book that we think is especially useful is our extensive presenta-
tion of statistical applications from the recent medical literature. Over 30 different
articles are explicated herein, taken from such journals as J ournal of the American
Medical Association, Journal of Urology, British Journal of Urology International,
American Journal of Epidemiology, Journal of Internal Medicine, Alcohol and
Alcoholism, and BMC Neurology . We deemed it important for readers to see how
the various techniques covered in the primer are employed, displayed, and discussed
in actual research. In the process we have attempted to “translate into English” some
of the more recondite terminology used in the literature. Hopefully, this enterprise
will facilitate the reader’s understanding of statistical applications when he or she
encounters them in the journals.
In the process of writing this primer, many people have been helpful to us. We
wish, fi rst, to acknowledge the kind guidance and cheerful fl exibility of Marc
Strauss, our editor at Springer. We also wish to thank Bowling Green State
University, in particular the Center for Family and Demographic Research, as well
as the University of Toledo Medical Center, for providing the computer and library
Preface ix
support that made this work possible. Also deserving of thanks are Annette Mahoney
and Kenneth I. Pargament in the Psychology Department at Bowling Green State
University for collecting the NAPPS data that are drawn on extensively in Chap. 9 .
And last, but certainly not least, we wish to gratefully acknowledge our wives,
Gabrielle and Linda, for the loving support and encouragement they provided dur-
ing the writing of this work. And now, let us begin…
Bowling Green , OH , USA Alfred DeMaris
Toledo , OH , USA Steven H. Selman