Cluster analysis using multivariate mixed effects models.

A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. Recently, methods have been proposed to deal with cases where individuals are classified into clusters through a linear model of mixed univariate effects deriving from a longitudinally measured variable. The method proposed in the current work deals with the case of clustering and then classification based on two or more variables measured longitudinally, through the fitting of non-linear multivariate mixed effect models, and with consideration given to parameter estimation for balanced and unbalanced data using an EM algorithm. The application of the method is illustrated with an example in which the clusters are identified and the classification into clusters is compared with the true membership of individuals in one of two groups, which is known at the end of the follow-up period.