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Introduction to Regression Methods for Public Health Using R [Kõva köide]

(Wright State University)
  • Formaat: Hardback, 442 pages, kõrgus x laius: 254x178 mm, kaal: 1000 g, 27 Tables, black and white; 45 Line drawings, color; 68 Line drawings, black and white; 45 Illustrations, color; 68 Illustrations, black and white
  • Ilmumisaeg: 19-Dec-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032203072
  • ISBN-13: 9781032203072
Teised raamatud teemal:
Introduction to Regression Methods for Public Health Using RSuurem pilt
  • Formaat: Hardback, 442 pages, kõrgus x laius: 254x178 mm, kaal: 1000 g, 27 Tables, black and white; 45 Line drawings, color; 68 Line drawings, black and white; 45 Illustrations, color; 68 Illustrations, black and white
  • Ilmumisaeg: 19-Dec-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032203072
  • ISBN-13: 9781032203072
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032203072.html
Märksõnad:

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


This book 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.

Arvustused

"Dr. Ramzi W. Nahhass book, Introduction to Regression Methods for Public Health Using R, is a timely and wellstructured resource aimed at public health students, researchers, and practitionerswho want to understand and apply regression techniques. Targeted at readers with limited formal statistical training, it also serves as a valuable refresher for more experienced users. [ ...] One of the books greatest strengths is its clear and logical structure, which makes it very accessible for beginners. [ ...] In conclusion, [ the book] is a highly practical resource that fills a gap in applied public health education. Its structure, clarity, and real-world focus make it ideal for students, early career researchers, and practitioners using R for regression. Its consistent structure and practical guidance make it a valuable reference." -Rachel Heyard in The American Statistician, March 2026

Preface
1. Introduction
2. Overview of Regression Methods
3. Data Summarization
4. Simple Linear Regression
5. Multiple Linear Regression
6. Binary Logistic Regression
7. Survival Analysis
8. Analyzing Complex Survey Data
9. Multiple Imputation of Missing Data Appendix A. Datasets Bibliography Index

Ramzi W. Nahhas teaches biostatistics at Wright State University, Dayton, Ohio, USA, where he is Professor in the Department of Population and Public Health Sciences, Boonshoft School of Medicine. In addition to teaching, he is actively involved in research collaborations with faculty, residents, and students, primarily in his own department and the Department of Psychiatry.