Skip to content

Analysis of Correlated Data

Course Number
5200 (Biostatistics)
Course Instructor(s)
Aya Mitani

Course Description

The course introduces statistical methods to analyze correlated data commonly encountered in health science research. Topics will include: data visualization, linear models for correlated data, linear mixed-effects models, marginal models, generalized linear mixed-effects models, multilevel models, missing data, and drop-out. Examples are extensively used to illustrate concepts and implemented using the software R. Students who are interested in the course can review the article Statistical Approaches to Longitudinal Data Analysis in  Neurodegenerative Diseases: Huntington’s Disease as a Model to have a more granular overview of the course materials.

Methods of Assessment

Homework 40%
Midterm exam 30%
Final project 30%

General Requirements

Biostatistics I (CHL5201H) and Biostatistics II (CHL5202H), or equivalent. Students are expected to understand linear regression and logistic regression.