Michael R. Chernick

The Essentials of Biostatistics for Physicians, Nurses, and Clinicians (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 228
• Erscheinungstermin: 4. August 2011
• Englisch
• ISBN-13: 9781118071939
• Artikelnr.: 37356159

Autorenporträt

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.

Inhaltsangabe

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