Buch, Englisch, 272 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 577 g
Buch, Englisch, 272 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 577 g
ISBN: 978-1-119-21248-5
Verlag: Wiley
A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology
This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usually covered in public health courses including (i) Applied Regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health.
In addition, this book:
• Is based on the authors’ course notes from 20 years teaching regression modeling in public health courses
• Provides exercises at the end of each chapter
• Contains a solutions chapter with answers in STATA, SAS, SPSS, and R
• Provides real-world public health applications of the theoretical aspects contained in the chapters
Applications of Regression Models in Epidemiology is a reference for graduate students in public health and public health practitioners.
ERICK SUÁREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. He received a Ph.D. degree in Medical Statistics from the London School of Hygiene and Tropical Medicine. He has 29 years of experience teaching biostatistics.
CYNTHIA M. PÉREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. She received an M.S. degree in Statistics and a Ph.D. degree in Epidemiology from Purdue University. She has 22 years of experience teaching epidemiology and biostatistics.
ROBERTO RIVERA is an Associate Professor at the College of Business at the University of Puerto Rico at Mayaguez. He received a Ph.D. degree in Statistics from the University of California in Santa Barbara. He has more than five years of experience teaching statistics courses at the undergraduate and graduate levels.
MELISSA N. MARTÍNEZ is an Account Supervisor at Havas Media International. She holds an MPH in Biostatistics from the University of Puerto Rico and an MSBA from the National University in San Diego, California. For the past seven years, she has been performing analyses for the biomedical research and media advertising fields.
Autoren/Hrsg.
Weitere Infos & Material
Preface xv
Acknowledgments xvii
About the Authors xix
1 Basic Concepts for Statistical Modeling 1
1.1 Introduction 1
1.2 Parameter Versus Statistic 2
1.3 Probability Definition 3
1.4 Conditional Probability 3
1.5 Concepts of Prevalence and Incidence 4
1.6 Random Variables 4
1.7 Probability Distributions 4
1.8 Centrality and Dispersion Parameters of a Random Variable 6
1.9 Independence and Dependence of Random Variables 7
1.10 Special Probability Distributions 7
1.10.1 Binomial Distribution 7
1.10.2 Poisson Distribution 8
1.10.3 Normal Distribution 9
1.11 Hypothesis Testing 11
1.12 Confidence Intervals 14
1.13 Clinical Significance Versus Statistical Significance 14
1.14 Data Management 15
1.14.1 Study Design 15
1.14.2 Data Collection 16
1.14.3 Data Entry 17
1.14.4 Data Screening 18
1.14.5 What to Do When Detecting a Data Issue 19
1.14.6 Impact of Data Issues and How to Proceed 20
1.15 Concept of Causality 21
References 22
2 Introduction to Simple Linear Regression Models 25
2.1 Introduction 25
2.2 Specific Objectives 26
2.3 Model Definition 26
2.4 Model Assumptions 28
2.5 Graphic Representation 29
2.6 Geometry of the Simple Regression Model 29
2.7 Estimation of Parameters 30
2.8 Variance of Estimators 31
2.9 Hypothesis Testing About the Slope of the Regression Line 32
2.9.1 Using the Student’s t-Distribution 32
2.9.2 Using ANOVA 32
2.10 Coefficient of Determination R2 34
2.11 Pearson Correlation Coefficient 34
2.12 Estimation of Regression Line Values and Prediction 35
2.12.1 Confidence Interval for the Regression Line 35
2.12.2 Prediction Interval of Actual Values of the Response 36
2.13 Example 36
2.14 Predictions 39
2.14.1 Predictions with the Database Used by the Model 40
2.14.2 Predictions with Data Not Used to Create the Model 42
2.14.3 Residual Analysis 44
2.15 Conclusions 46
Practice Exercise 47
References 48
3 Matrix Representation of the Linear Regression Model 49
3.1 Introduction 49
3.2 Specific Objectives 49
3.3 Definition 50
3.3.1 Matrix 50
3.4 Matrix Representation of a SLRM 50
3.5 Matrix Arithmetic 51
3.5.1 Addition and Subtraction of Matrices 51
3.6 Matrix Multiplication 52
3.7 Special Matrices 53
3.8 Linear Dependence 54
3.9 Rank of a Matrix 54
3.10 Inverse Matrix [A-1] 54
3.11 Application of an Inverse Matrix in a SLRM 56
3.12 Estimation of ß Parameters in a SLRM 56
3.13 Multiple Linear Regression Model (MLRM) 57
3.14 Interpretation of the Coefficients in a MLRM 58
3.15 ANOVA in a MLRM 58
3.16 Using Indicator Variables (Dummy Variables) 60
3.17 Polynomial Regression Models 63
3.18 Centering 64
3.19 Multicollinearity 65
3.20 Interaction Terms 65
3.21 Conclusion 66
Practice Exercise 66
References 67
4 Evaluation of Partial Tests of Hypotheses in a MLRM 69
4.1 Introduction 69
4.2 Specific Objectives 69
4.3 Definition of Partial Hypothesis 70
4.4 Evaluation Process of Partial Hypotheses 71
4.5 Special Cases 71
4.6 Examples 72
4.7 Conclusion 75
Practice Exercise 75
References 75
5 Selection of Variables in a Multiple Linear Regression Model 77
5.1 Introduction 77
5.2 Specific Objectives 77
5.3 Selection of Variables According to the Study Objectives 77
5.4 Criteria for Selecting the Best Regression Model 78
5.4.1 Coefficient of Determination, R2 78
5.4.2 Adjusted Coefficient of Determination, R2A 78
5.4.3 Mean Squar
