The Essentials of Biostatistics for Physicians, Nurses, and Clinicians

225 Pages·2013·2.1 MB·English

The Essentials of
Biostatistics for Physicians,
Nurses, and Clinicians
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The Essentials of
Biostatistics for Physicians,
Nurses, and Clinicians
Michael R. Chernick
Lankenau Institute for Medical Research
Wynnewood, PA
A John Wiley & Sons, Inc., Publication
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Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved.
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Library of Congress Cataloging-in-Publication Data:
Chernick, Michael R.
The essentials of biostatistics for physicians, nurses, and clinicians / Michael R.
Chernick.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-0-470-64185-9 (pbk.)
1. Biometry. I. Title.
[DNLM: 1. Biostatistics. WA 950]
QH323.5C484 2011
570.1'5195–dc22
2011002198
oBook ISBN: 978-1-118-07195-3
ePDF ISBN: 978-1-118-07193-9
ePub ISBN: 978-1-118-07194-6
Printed in Singapore.
10 9 8 7 6 5 4 3 2 1
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Contents
Preface ix
1. The What, Why, and How of Biostatistics in
Medical Research 1
1.1 Defi nition of Statistics and Biostatistics, 1
1.2 Why Study Statistics?, 3
1.3 The Medical Literature, 9
1.4 Medical Research Studies, 11
1.4.1 Cross-sectional studies including surveys, 11
1.4.2 Retrospective studies, 12
1.4.3 Prospective studies other than clinical trials, 12
1.4.4 Controlled clinical trials, 12
1.4.5 Conclusions, 13
1.5 Exercises, 14
2. Sampling from Populations 15
2.1 Defi nitions of Populations and Samples, 17
2.2 Simple Random Sampling, 18
2.3 Selecting Simple Random Samples, 19
2.4 Other Sampling Methods, 27
2.5 Generating Bootstrap Samples, 28
2.6 Exercises, 32
3. Graphics and Summary Statistics 34
3.1 Continuous and Discrete Data, 34
3.2 Categorical Data, 35
3.3 Frequency Histograms, 35
3.4 Stem-and-Leaf Diagrams, 38
3.5 Box Plots, 39
3.6 Bar and Pie Charts, 39
3.7 Measures of the Center of a Distribution, 42
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vi Contents
3.8 Measures of Dispersion, 46
3.9 Exercises, 50
4. Normal Distribution and Related Properties 51
4.1 Averages and the Central Limit Theorem, 51
4.2 Standard Error of the Mean, 53
4.3 Student’s t-Distribution, 53
4.4 Exercises, 55
5. Estimating Means and Proportions 58
5.1 The Binomial and Poisson Distributions, 58
5.2 Point Estimates, 59
5.3 Confi dence Intervals, 62
5.4 Sample Size Determination, 65
5.5 Bootstrap Principle and Bootstrap Confi dence
Intervals, 66
5.6 Exercises, 69
6. Hypothesis Testing 72
6.1 Type I and Type II Errors, 73
6.2 One-Tailed and Two-Tailed Tests, 74
6.3 P-Values, 74
6.4 Comparing Means from Two Independent Samples:
Two-Sample t-Test, 75
6.5 Paired t-Test, 76
6.6 Testing a Single Binomial Proportion, 78
6.7 Relationship Between Confi dence Intervals and Hypothesis
Tests, 79
6.8 Sample Size Determination, 80
6.9 Bootstrap Tests, 81
6.10 Medical Diagnosis: Sensitivity and Specifi city, 82
6.11 Special Tests in Clinical Research, 83
6.11.1 Superiority tests, 84
6.11.2 Equivalence and bioequivalence, 84
6.11.3 Noninferiority tests, 86
6.12 Repeated Measures Analysis of Variance and Longitudinal
Data Analysis, 86
6.13 Meta-Analysis, 88
6.14 Exercises, 92
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Contents vii
7. Correlation, Regression, and Logistic
Regression 95
7.1 Relationship Between Two Variables and the Scatter
Plot, 96
7.2 Pearson’s Correlation, 99
7.3 Simple Linear Regression and Least Squares
Estimation, 101
7.4 Sensitivity to Outliers and Robust Regression, 104
7.5 Multiple Regression, 111
7.6 Logistic Regression, 117
7.7 Exercises, 122
8. Contingency Tables 127
8.1 2 × 2 Tables and Chi-Square, 127
8.2 Simpson’s Paradox in the 2 × 2 Table, 129
8.3 The General R × C Table, 132
8.4 Fisher’s Exact Test, 133
8.5 Correlated Proportions and McNemar’s Test, 136
8.6 Relative Risk and Odds Ratio, 138
8.7 Exercises, 141
9. Nonparametric Methods 145
9.1 Ranking Data, 146
9.2 Wilcoxon Rank-Sum Test, 146
9.3 Sign Test, 149
9.4 Spearman’s Rank-Order Correlation Coeffi cient, 150
9.5 Insensitivity of Rank Tests to Outliers, 153
9.6 Exercises, 154
10. Survival Analysis 158
10.1 Time-to-Event Data and Right Censoring, 159
10.2 Life Tables, 160
10.3 Kaplan–Meier Curves, 164
10.3.1 The Kaplan–Meier curve: a nonparametric estimate
of survival, 164
10.3.2 Confi dence intervals for the Kaplan–Meier
estimate, 165
10.3.3 The logrank and chi-square tests: comparing two
or more survival curves, 166
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viii Contents
10.4 Parametric Survival Curves, 168
10.4.1 Negative exponential survival distributions, 168
10.4.2 Weibull family of survival distributions, 169
10.5 Cox Proportional Hazard Models, 170
10.6 Cure Rate Models, 171
10.7 Exercises, 173
Solutions to Selected Exercises 175
Appendix: Statistical Tables 192
References 204
Author Index 209
Subject Index 211
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Preface
I
have taught biostatistics in the health sciences and published a book
in 2003 with Wiley on that topic. That book is a textbook for upper -
level undergraduates and graduate students in the health science depart-
ments at universities. Since coming to the Lankenau Institute 17 months
ago, I was tasked to prepare a course in biostatistics for nurses and
physicians (particularly the hospital residents and fellows that do
medical research). I quickly learned that although the material in my
book was relevant, it contained too much material and was not in a
digestible form for them. I prepared a six - lecture course (1 hour each)
for physicians, and a two - lecture course for the nurses. To prevent
boredom, I introduced some funny but educational cartoon slides. The
course currently exists and has been refi ned as PowerPoint presenta-
tions and has been moderately successful. I also am starting a similar
course at statistics.com.
The physicians and nurses have a busy schedule, and what they
need is a concise and clearly explained set of lectures that cover only
the areas of statistics that are essential to know about in medical
research. This means topics that are not taught in traditional introduc-
tory statistics courses. So Kaplan – Meier curves, repeated measures
analysis of variance, hazard ratios, contingency tables, logrank tests,
bioequivalence, cross - over designs, noninferiority, selection bias, and
group sequential methods are all included, but they are introduced on
a conceptual level without the need for theory. It is when and why these
methods work that they need to know, and not a detailed account of
how they work mathematically. I feel that it would be appropriate to
have a textbook for such a course that can be taught in - house at research
centers or online courses. The book is intended to be approximately
160 pages along with suitable references.
ix
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x Preface
I am very grateful to Professor Marlene Egger, who carefully
reviewed the manuscript and made several wonderful suggestions that
helped with the clarity and improved the content of the book.
Michael R. Chernick
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