References and Further Reading

Contents

References and Further Reading#

Primary Datasets#

The worked examples throughout this book utilize two historically significant datasets representing observational and experimental epidemiology:

1. The Framingham Heart Study Teaching Subset

National Heart, Lung, and Blood Institute. Framingham Heart Study Teaching Dataset. Bethesda, MD: NHLBI; Provided for educational use under the NHLBI teaching dataset programme (N01-HC-25195).

2. The Anorexia Clinical Trial Dataset

Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J., and Ostrowski, E. (1994). A Handbook of Small Data Sets. London: Chapman and Hall. Distributed via the MASS package in R.

Software#

R Core Team. (2026). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.r-project.org/

Posit Team. (2026). RStudio: Integrated Development Environment for R. Posit Software, PBC. https://posit.co/

GNU Project. (2026). PSPP: A Program for Statistical Analysis of Sampled Data. Free Software Foundation. https://www.gnu.org/software/pspp/

Recommended Further Reading#

The following texts are recommended for students who wish to go deeper after completing this course.

Biostatistics and Epidemiology#

Gordis, L. (2014). Epidemiology (5th ed.). Elsevier Saunders. The standard epidemiology textbook used in public health programmes worldwide. Chapters 5–9 extend the hypothesis testing and relative risk concepts covered here.

Rosner, B. (2015). Fundamentals of Biostatistics (8th ed.). Cengage Learning. A rigorous treatment of every method in this course with clinical worked examples.

Szklo, M., & Nieto, F. J. (2019). Epidemiology: Beyond the Basics (4th ed.). Jones and Bartlett. Covers effect modification, confounding, and causal inference — the natural next step after this course.

Statistical Computing#

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd ed.). O’Reilly. Freely available at https://r4ds.hadley.nz/ The definitive introduction to data wrangling and visualisation in R using the tidyverse.

Navarro, D. (2019). Learning Statistics with R. Freely available at https://learningstatisticswithr.com/ A complete introductory statistics course taught entirely in R, with exceptional clarity on hypothesis testing and regression.

Research Ethics#

National Commission for the Protection of Human Subjects. (1979). The Belmont Report. U.S. Department of Health and Human Services. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/

World Medical Association. (2013). Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. https://www.wma.net/policies-post/wma-declaration-of-helsinki/

Academic Citation for This Book#

If you use or adapt material from this book in your own work, please cite it as:

Yau, P. T. O., Puvanendran, S., and Nualyong, J. (2026). A Little Bit of Everything in Biostatistics for Health Science Students. Published online at: https://paytonyau.github.io/biostats-book