Latent growth modeling approaches, such as growth mixture models, are used to identify meaningful groups or classes of individuals in a larger heterogeneous population. But when applied to multivariate repeated measures computational problems are likely, due to the high dimension of the joint distribution of the random effects in these mixed-effects models. This article proposes a cluster algorithm for multivariate repeated data, using pseudo-likelihood and ideas based on k-means clustering, to reveal homogenous subgroups. The algorithm was demonstrated on an electro-encephalogram dataset set quantifying the effect of psychoactive compounds on the brain activity in rats.
Keywords: Cluster analysis; EEG data; joint models; multivariate longitudinal data.