Multivariable Analysis with R
Interested in learning how to examine the relationship between multiple variables and an outcome of interest? Multivariable regression analysis is a foundational statistical technique that allows researchers to account for potential confounding factors, evaluate associations, and generate more robust evidence from their data. Through live demonstrations and hands-on exercises in R, participants will gain practical experience developing, evaluating, and interpreting multivariable regression models.
This workshop introduces the complete workflow for conducting multivariable analyses in R, from data preparation and model building to diagnostic testing and research reporting. Whether you are conducting quantitative research, analyzing observational data, or looking to strengthen your statistical skills, this session will provide a practical foundation for applying multivariable regression methods in a research context.
This session is ideal for researchers, graduate students, and analysts seeking to strengthen their quantitative research skills. Basic familiarity with R and introductory statistical concepts is recommended.
By the end of this workshop, participants will be able to:
- Prepare and clean research datasets for multivariable analysis in R
- Fit and interpret multivariable linear and logistic regression models using R
- Assess model assumptions, diagnose common issues
- Select appropriate variables and build multivariable models using practical modeling strategies
- Create publication-ready tables, figures, and model summaries for research reporting
Workshop Preparation
None
Facilitator Bio
Shubrandu (he/him) is a PhD Candidate in Health Research Methodology (Clinical Epidemiology) at McMaster University with a background in epidemiology and biostatistics. He has extensive experience working with large healthcare datasets and supports researchers in study design, statistical analysis, and data management. His expertise includes regression modeling, survival analysis, and handling administrative health data, with proficiency in R and SAS.
Workshop Slides
Coming soon.