The Essentials of Biostatistics for Physicians, Nurses, and Clinicians / Edition 1

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Overview

A fundamental and straightforward guide to using and understanding statistical concepts in medical research

Designed specifically for healthcare practitioners who need to understand basic biostatistics but do not have much time to spare, The Essentials of Biostatistics for Physicians, Nurses and Clinicians presents important statistical methods used in today's biomedical research and provides insight on their appropriate application. Rather than provide detailed mathematics for each of these methods, the book emphasizes what healthcare practitioners need to know to interpret and incorporate the latest biomedical research into their practices.

The author draws from his own experience developing and teaching biostatistics courses for physicians and nurses, offering a presentation that is non-technical and accessible. The book begins with a basic introduction to the relationship between biostatistics and medical research, asking the question "why study statistics?," while also exploring the significance of statisitcal methods in medical literature and clinical trials research. Subsequent chapters explore key topics, including:

  • Correlation, regression, and logistic regression
  • Diagnostics
  • Estimating means and proportions
  • Normal distribution and the central limit theorem
  • Sampling from populations
  • Contingency tables
  • Meta-analysis
  • Nonparametric methods
  • Survival analysis

Throughout the book, statistical methods that are often utilized in biomedical research are outlined, including repeated measures analysis of variance, hazard ratios, contingency tables, log rank tests, bioequivalence, cross-over designs, selection bias, and group sequential methods. Exercise sets at the end of each chapter allow readers to test their comprehension of the presented concepts and techniques.

The Essentials of Biostatistics for Physicians, Nurses, and Clinicians is an excellent reference for doctors, nurses, and other practicing clinicians in the fields of medicine, public health, pharmacy, and the life sciences who need to understand and apply statistical methods in their everyday work. It also serves as a suitable supplement for courses on biostatistics at the upper-undergraduate and graduate levels.

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Editorial Reviews

From the Publisher
"This book should be very useful for its intended readers, but it would be helpful if they had a beginning understanding of basic statistics and terminology." (Doody's, 10 February 2012)
Doody's Review Service
Reviewer: Diane M Tomasic, EdD, RN (Slippery Rock University )
Description: This book presents a nontechnical approach to the essential biostatics used in medical research, covering topics not necessarily taught in traditional introductory statistics courses, explaining them on a conceptual level without the theoretical background or how they work mathematically.
Purpose: The purpose is to provide readers with the knowledge they need to understand biostatics and their application in medical research. The emphasis is on how to interpret biostatistics and incorporate medical research into practice.
Audience: The target audience is nurses and physicians, hospital residents, and fellows. The author recommends this book as a supplement for biostatics courses at the upper-undergraduate and graduate levels. It also can serve as a resource for healthcare professionals. The author, who has over 30 years in the field of biostatics, teaches biostatistics and has published a book on the subject.
Features: A basic introduction to the relationship between biostatics and medical research begins the book. The significance of statistical methods in medical literature and clinical trials research is explored. Key concepts covered include correlation, regression, and logistic regression, diagnostics, estimating means and proportions, normal distribution and the central limit theorem, sampling from populations, contingency tables, meta-analysis, nonparametric methods, survival analysis, repeated measures analysis of variance, hazard ratios, contingency tables, log rank tests, bioequivalence, crossover designs, selection bias, and group sequential method. Chapter exercises test for comprehension.
Assessment: This book should be very useful for its intended readers, but it would be helpful if they had a beginning understanding of basic statistics and terminology.
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Product Details

  • ISBN-13: 9780470641859
  • Publisher: Wiley
  • Publication date: 9/27/2011
  • Series: Wiley Desktop Editions Series
  • Edition number: 1
  • Pages: 228
  • Sales rank: 765,801
  • Product dimensions: 6.10 (w) x 9.20 (h) x 0.60 (d)

Meet the Author

Michael R. Chernick, PhD, is Manager of Biostatistical Services at Lankenau Institute for Medical Research, where he conducts statistical design and analysis for pharmaceutical research. He has more than thirty years of experience in the application of statistical methods to such areas as medicine, energy, engineering, insurance, and pharmaceuticals. Dr. Chernick is the author of Bootstrap Methods: A Guide for Practitioners and Researchers, Second Edition, and the coauthor of Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap, both published by Wiley.

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Table of 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 Definitions 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

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 Confidence 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 Specificity, 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

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 x 2 Tables and Chi-Square, 127

8.2 Simpson's Paradox in the 2 x 2 Table, 129

8.3 The General R x 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 Coefficient, 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 Confidence intervals for the Kaplan-Meier estimate, 165

10.3.3 The logrank and chi-square tests: comparing two or more survival curves, 166

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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