Introduction to R for Medical Decision Modelers

SMDM Virtual Course
Length:  4 hours
Target audience: Beginners

June 04, 2024



R is a freely available programming language that has become increasingly popular, and useful, for data analysis and decision modeling. Starting with the basics, this course will provide foundational knowledge to start using R effectively in your research. First, we’ll cover how to install and navigate R and RStudio. Next, you’ll learn fundamental data management skills, enabling you to organize and manipulate data efficiently. You'll also learn how to create compelling graphics to visualize your findings. Through practical exercises, you'll gain proficiency in simple data analysis techniques, including linear and logistic regressions. Additionally, we'll provide a primer on "advanced topics" such as loops, functions, and matrix algebra, empowering you to tackle more complex data tasks. Lastly, we will provide tips for debugging code and troubleshooting common error messages.

With an expanding array of packages introducing advanced techniques relevant to economic evaluation or data science, R offers significant potential for exploration. Starting with the basics, this course provides the foundational knowledge required to apply these advanced techniques effectively in the future.

Learning Objectives: By the end of the course, participants will have the skills and confidence to do the following using R:

  • Navigate R and RStudio (e.g., installation, create a ‘project’, create a GitHub repository for code sharing)

  • Implement common data manipulation techniques (e.g., creating and re-coding variables, identifying and replacing missing values, creating subsets of datasets)

  • Create different types of graphics using sample data (e.g., histogram, scatterplot, line graph, bar chart, survival curve)

  • Conduct simple types of data analyses (e.g., logistic and linear regression models)

  • Familiarity with advanced coding techniques (e.g., loops, functions, matrix algebra)

All materials of this short course will be provided to participants after the course for future use.


Chaoran Dong is a PhD candidate in Health Services Research at the University of Toronto. Her research primarily focuses on economic evaluations aimed at serving vulnerable populations, with a special emphasis on resource allocation to improve access to healthcare services. She holds a Master of Science in Public Health from the Johns Hopkins University.



Alexandra Moskalewicz is a Research Analyst at the Hospital for Sick Children, supporting research studies on the long-term health and economic impacts of childhood cancer. She holds a Master of Science in Health Services Research from the University of Toronto.




Petros Pechlivanoglou, PhD, is a Senior Scientist at The Hospital for Sick Children’s Research Institute and an Associate Professor at the University of Toronto’s Institute of Health Policy, Management and Evaluation. His research interests include: the use of health decision analysis in economic evaluation; bridging evidence synthesis, administrative data and decision analysis; and the application and extension of predictive models in health economics. Dr. Pechlivanoglou is also a member of DARTH working group, a collaborative that aims to expand the knowledge about R for open-source, transparent decision analytic modelling.



USD 425 for Non-Members
USD 280 for Members
USD 150 for Trainee, LMIC or Bridge Members


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