Acknowledgments and resources

We are genuinely grateful to many people within the Statistics, Machine Learning, Epidemiology and R programming communities, who shared their valuable work, open source software, and training resources.

Licensing and use of the workshop materials

The workshop materials are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. All borrowed external materials (images, worked examples, etc.), are credited with proper “Source” statements and governed by their own licenses. To our knowledge, all the consulted materials were published under “open access” or “creative commons” frameworks. If this were not the case for any content piece displayed here, please let us know and it will be removed.

Selected resources for self-guided learning

Below is a curated list of great resources (most of which free and openly accessible) you can peruse on your own.

Statistics, Biostatistics, Epidemiology with R examples

• Choueiry, G. (2024, December 5). 8 Types of Treatment Effects Explained (with Examples) [Article]. https://quantifyinghealth.com/treatment-effects-explained/
  
• Heiss, A. (2024, March 21). Demystifying causal inference estimands: ATE, ATT, and ATU [Article]. Andrew Heiss’s blog. https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu
  
• Greifer, N., & Stuart, E. A. (2021). Choosing the Causal Estimand for Propensity Score Analysis of Observational Studies [Article]. https://doi.org/10.48550/ARXIV.2106.10577
• Applied Epi Team (2024). Applied Epi - Elevating frontline epidemiology [Course]. Training, support, tools. https://www.appliedepi.org/
• Applied Epi Team (2024). R for applied epidemiology and public health The Epidemiologist R Handbook [Course]. https://epirhandbook.com/en/
• Bobbitt, Z. (2024). Statology [Course]. Statology. https://www.statology.org/
• Çetinkaya-Rundel, M., & Hardim, J. (2023). Introduction to Modern Statistics (1st Ed) [Book]. https://openintro-ims.netlify.app/
• childs_introductory_2022-1?
• Selby, D., & 2021 (2024). Analytical Epidemiology II [Course]. David Selby. https://personalpages.manchester.ac.uk/staff/david.selby/analysis/2021-03-30-inference/
  
• Vu, J., & Harrington, D. (2021). Introductory Statistics for the Life and Biomedical Sciences [Book]. https://www.openintro.org/book/biostat/

R packages & tools

• POSIT (2024). POSIT resources [R]. https://posit.co/resources/.
• RStudio, & Posit (2024). Base-r-cheat-sheet.pdf [R]. https://iqss.github.io/dss-workshops/R/Rintro/base-r-cheat-sheet.pdf
• Various contributors (2024). Bioconductor - Open source software for Bioinformatics [R]. Bioconductor - Open source software for Bioinformatics. https://www.bioconductor.org/
• Wickham, H., François, R., Müller, K., & Vaughan, D. (2023). Programming with dplyr [R]. dplyr. https://dplyr.tidyverse.org/articles/programming.html#tidy-selection
• Wickham, H. (2014). Tidy Data [R]. Journal of Statistical Software, 59(10). https://doi.org/10.18637/jss.v059.i10

Sources of practice datasets

• Vanderbilt Department of Biostatistics (2023, September 17). Vanderbilt Biostatistics Datasets [Dataset]. https://hbiostat.org/data/
• Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone [Dataset]. BMC Medical Informatics and Decision Making, 20(1), 16. https://doi.org/10.1186/s12911-020-1023-5
• Ahmad, T., Munir, A., Bhatti, S. H., Aftab, M., & Raza, M. A. (2017). Survival analysis of heart failure patients: A case study [Dataset]. PLOS ONE, 12(7), e0181001. https://doi.org/10.1371/journal.pone.0181001
• Joosten, H., Van Eersel, M. E. A., Gansevoort, R. T., Bilo, H. J. G., Slaets, J. P. J., & Izaks, G. J. (2013). Cardiovascular Risk Profile and Cognitive Function in Young, Middle-Aged, and Elderly Subjects [Dataset]. Stroke, 44(6), 1543–1549. https://doi.org/10.1161/STROKEAHA.111.000496