498 – Correlated/Clustered Data Analysis
Covariance Structures for Multiple Repeated Measures Models
Hongmei Han
Pennington Biomedical Research Center
Jeff Burton
Pennington Biomedical Research Center
Robbie Beyl
Pennington Biomedical Research Center
Lei Zhang
Mississippi State Department of Health
William D. Johnson
Pennington Biomedical Research Center
Consider a statistical model with a single outcome that is observed repeatedly under different circumstances. In a study employing an oral glucose tolerance test, for example, serum glucose levels may be measured just prior to ingesting a dose of glucose and then repeatedly every 30 minutes for a total of 120 minutes. The aim is to determine the pattern of change in glucose levels as an indication of how efficiently the individual is disposing the glucose from the blood. If an intervention is given and the process is repeated under this new condition, the data may be analyzed using a doubly repeated measures model. The use of structured patterns in the underlying covariance matrix for correlated residuals in statistical models involving repeated measures is well documented. In models involving multiply repeated measures, however, use of different covariance patterns for different conditions has not been fully discussed. The purpose of this paper is to fill that need by presenting an illustrative overview in terms of practical examples.