Longitudinal studies follow individuals over time and repeatedly measure health status, which facilitates prospective ascertainment of exposures and incident outcomes, and identification of changes over time within individuals. Analyses of longitudinal data must account for the correlation that arises from collecting repeated measures on the same individuals over time.

This module will introduce statistical methods for the analysis of longitudinal data, with a focus on marginal (or, population-averaged) models fit via generalized estimating equations and conditional (or, subject-specific) models fit via generalized linear mixed-effects models. Relevant theoretical background will be provided. Illustrative examples and interactive activities (conducted in R) will be used to practice analysis approaches, modeling strategies, and interpretation of results.

This course is targeted toward individuals with little or no prior experience with statistical methods for longitudinal data analysis. Experience with using regression methods to analyze data (e.g., linear regression, logistic regression) is important background for this module. Some experience in R will be helpful for full engagement during activities.