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Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.
The text includes an overview of regression (Chapter 2) how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6) Cox proportional hazards regression (survival analysis) (Chapter 7) handling data arising from a complex survey design (Chapter 8) and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.
Key Features:
Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing data
Accessible to those with only a first course in statistics
Serves as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analyses
Includes examples of how to diagnose the fit of a regression model
Includes examples of how to summarize, visualize, table, and write up the results
Includes R code to run the examples
Preface
Introduction
Overview of Regression Methods
Data Summarization
Simple Linear Regression
Multiple Linear Regression
Binary Logistic Regression
Survival Analysis
Analyzing Complex Survey Data
Multiple Imputation of Missing Data
Appendix A. Datasets
Bibliography
Index