Correlated Data Analysis using R
Monday, May 13, 2024
Dalla Lana School of Public Health, University of Toronto

About this event

The Division of Biostatistics in the Dalla Lana School of Public Health at the University of Toronto is pleased to host the SORA-TABA Annual Workshop & DLSPH Biostatistics Research Day. The event brings together regional and local statistical communities who are interested in biostatistics, artificial intelligence, data science, financial statistics, and other applied areas of statistics. Please join us in making this event a great success!

Assistant Professor Aya Mitani from the University of Toronto, Division of Biostatistics, will lead the workshop on Correlated data analysis using R to be held Monday, May 13th at the Dalla Lana School of Public Health, University of Toronto (155 College Street) btw 9:00am – 5:00pm. Virtual registration options are available.

In addition to the workshop, the event will showcase student research posters. Students and post-docs are encouraged to present their research or practicum work, and three Paul Corey Memorial Student Poster Presentation Awards will be awarded.

A career panel will be held during the lunch break. This will be of particular interest to students and early-career biostatisticians.

Please provide poster abstracts for review on or before Friday, April 26th at 5pm by completing the online Abstract Submissions form and emailing your abstract to biostat.dlsph@utoronto.ca. Information for prospective poster presenters, including submission timelines, poster dimensions and the assessment rubric is provided within the form. Poster presenters are not required to register for the workshop but must be available in-person during the poster presentation period. If you have any questions, please email Ryan Rosner at biostat.dlsph@utoronto.ca.

To register and learn more about the workshop, please visit: 2024 SORA-TABA Annual Workshop & DLSPH Biostatistics Research Day.

2024 SORA-TABA Annual Workshop & DLSPH Biostatistics Research Day www.eventbrite.ca

Correlated Data Analysis using R

Instructor:

Assistant Professor Aya Mitani
University of Toronto, Division of Biostatistics

Abstract:

Correlated data are commonly encountered in both observational and experimental studies. The key feature of correlated data is the repeated measurement of the same variable within each individual or group. These measurements are typically correlated within the same individual or group, and the analysis of such data must consider the possible correlation. In this workshop, I will provide an overview of methods including marginal models and mixed effects models for both continuous and categorical responses. I will also go over ways to handle dropout in longitudinal studies. We will use the R statistical programming language for all examples. Participants should have familiarity with R and a basic statistical background that includes a good understanding of linear and generalized linear models.

Program:

Aya Mitani is Assistant Professor in the Division of Biostatistics at the Dalla Lana School of Public Health (DLSPH), University of Toronto. Her research focuses on developing statistical methods to analyze correlated data and to remove biases that emerge from informative cluster size, study design, missing data, or misclassification in multilevel observational studies and complex surveys. She has collaborated with researchers from oncology, nephrology, anesthesiology, and oral health. Aya teaches “Analysis of Correlated Data” at DLSPH.

Agenda

• 08:15-9:00 Registration & Breakfast
• 09:00-09:15 Opening Remarks
• 09:15-10:30 Module 1
• 10:30-10:45 Morning Break
• 10:45-12:00 Module 2
• 12:00-13:30 Lunch, Poster presentation assessment & Career Panel
• 13:30-15:00 Module 3
• 15:00-15:15 Afternoon Break
• 15:15-16:45 Module 4
• 16:45-17:00 Closing Remarks

***Registrants will be provided access to recordings of the workshop for three weeks following the event***