BIOE 866 - Linear Mixed Models

Cr Hrs: 2 (32-0-0)
This course provides the advanced skills necessary for independent statistical analysis of epidemiologic and clinical data containing clustered observations and random effects. Topics to be covered include unrestricted and restricted maximum likelihood estimation, Akaike’s information criterion, standard general linear models, linear random effects models, linear covariance pattern models, and linear random coefficient models. The course focuses on applications requiring flexible modeling of variance and covariance structures for clustered data when observations from a common cluster are correlated. The approaches covered in the course are particularly relevant for analysis of hierarchical and longitudinal data having Gaussian distributed error.

Grade Mode: Standard
Instructional Method: Lecture
Prerequisites: BIOE 821, BIOE 822, instructor’s consent

Term offered: Fall

Fall - Instructor of Record: Tamekia Jones
Fall - Additional Instructor 1: Jim Wan