Registration for this module closes July 13. 

Live sessions will be held 8:30 a.m. - noon Pacific (11:30-3 p.m. Eastern).

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 generalized linear mixed-effects models, which combine a model for the mean response with a model for population heterogeneity. Relevant theoretical background will be provided. An illustrative example (conducted in R) will be used to illustrate analysis approaches, modeling strategies, and interpretation of results.

This course is targeted toward individuals with some prior experience with statistical methods for longitudinal data analysis. Individuals without such experience should consider Module 10: Generalized Estimating Equations for Longitudinal Data Analysis. Experience with using regression methods to analyze data (e.g., linear regression, logistic regression) is important background for this module.