Analysis of Longitudinal Data: Comparison between PROC GLM and PROC MIXED

Longitudinal data refers to datasets with multiple measurements of a response variable on the same experimental unit made over a period of time. These types of data require special attention because they involve correlated data. The relationships between repeated measurements are important in assessing reliability and tracking of those measurements. The proper variancecovariance structure in the analysis model is essential to the understanding and interpretation of those relationships. The assumption of compound symmetry necessary for correctly using the intraclass correlation as a measure of tracking can be tested against other variance structures using PROC MIXED. This paper compares the variance, covariance and correlation estimates obtained from the GLM and MIXED procedures of SAS/STAT® on two sets of data, one of which has missing data.