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Analyzing Multivariate Longitudinal Data Using SAS

• Tags:
• measured,
• correlated,
• longitudinal,
• multivariate

Overview Comparing to the traditional univariate longitudinal data, the analysis of multivariate longitudinal data can be challenging because the variances of errors are likely to be different for different markers, the errors are likely to be correlated for the same marker measured at different occasions, and the errors are also likely to be correlated among markers measured at the same time. This paper, with application to a real-world study to evaluate the joint evolution of the biomarkers for renal structure and function, illustrates and compares 3 different approaches provided by SAS to analyze multivariate longitudinal data: the multivariate repeated measurement model with a Kronecker product covariance (PROC MIXED), the random coefficient mixed model (PROC MIXED) and the structural equation modeling approach (PROC CALIS).

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