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This is a SECOND DRAFT but is still awaiting peer review. The goal is publication as a printed version (through CRC Press) with the online version remaining freely available.

Status

  • Chapters 2-9: Sent out for peer review

If you have any comments or suggestions, feel free to contact me at . Thank you!

Use

Creative Commons License
Introduction to Regression Methods for Public Health using R by Ramzi W. Nahhas is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Preface

This text is suitable as a second biostatistics course for Master of Public Health students or public health professionals. Almost all public health students take an introductory biostatistics course, providing foundational competencies but perhaps not enough to use more advanced methods without additional training. There are a plethora of textbooks covering topics such as linear regression, logistic regression, and survival analysis aimed at those with a background in mathematical statistics and/or without a focus specifically on public health and/or without a focus on using R. The goal of this text is to provide a gentle introduction to regression methods, using R, that covers all the basics and a bit more with examples drawn from public health data. My hope is that what you learn here will give you the knowledge and skills to understand and carry out appropriate basic regression analyses and the foundation and confidence to go deeper. When you are ready to go deeper, there are excellent texts that cover each of the methods covered herein, as well as R programming, in much greater detail (e.g., Faraway 2016; Fox 2015; Fox and Weisberg 2019; Harrell 2015; Klein and Moeschberger 2010; Kleinman and Horton 2014; Lohr 2021; Lumley 2010; van Buuren 2018; Weisberg 2013; H. Wickham, Çetinkaya-Rundel, and Grolemund 2017; Hadley Wickham 2019).

Software information and conventions

The knitr package (Y. Xie 2015) and the bookdown package (Yihui Xie 2023) were used to compile this text. Package names and inline code are formatted in a typewriter font (e.g., knitr, lm(Y ~ X)), and function names are followed by parentheses (e.g., lm()).

Acknowledgments

To write

References

———. 2016. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. 2nd ed. Boca Raton, London, New York: Chapman; Hall/CRC.
Fox, John. 2015. Applied Regression Analysis and Generalized Linear Models. 3rd ed. Los Angeles: Sage Publications, Inc.
Fox, John, and Sanford Weisberg. 2019. An R Companion to Applied Regression. 3rd ed. Los Angeles: Sage Publications, Inc.
Harrell, Frank E, Jr. 2015. Regression Modeling Strategies. 2nd ed. Switzerland: Springer International Publishing.
Klein, John P., and Melvin L. Moeschberger. 2010. Survival Analysis: Techniques for Censored and Truncated Data. New York, NY: Springer.
Kleinman, Ken, and Nicholas J. Horton. 2014. SAS and R. 2nd ed. Boca Raton: Routledge.
Lohr, Sharon L. 2021. Sampling: Design and Analysis. 3rd ed. Boca Raton: Chapman; Hall/CRC.
———. 2010. Complex Surveys: A Guide to Analysis Using r: A Guide to Analysis Using r. John Wiley; Sons.
van Buuren, Stef. 2018. Flexible Imputation of Missing Data. 2nd ed. Boca Raton: Chapman; Hall/CRC.
Weisberg, Sanford. 2013. Applied Linear Regression. 4th ed. Hoboken, NJ: Wiley.
Wickham, Hadley. 2019. Advanced R. 2nd ed. Boca Raton London New York: Chapman; Hall/CRC.
Wickham, H., M. Çetinkaya-Rundel, and G. Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. Sebastopol, CA: O’Reilly Media.
Xie, Y. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.org/knitr/.
Xie, Yihui. 2023. Bookdown: Authoring Books and Technical Documents with r Markdown. https://CRAN.R-project.org/package=bookdown.