title: A clustering algorithm for multivariate longitudinal data.
abstract: 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.
authors.affiliation: a I-BioStat , Universiteit Hasselt , Diepenbeek , Belgium.
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Bruckers
authors.affiliation: a I-BioStat , Universiteit Hasselt , Diepenbeek , Belgium.|b I-BioStat , Katholieke Universiteit Leuven , Leuven , Belgium.
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
authors.affiliation: c Janssen Research & Development , Division of Janssen Pharmaceutica NV , Beerse , Belgium.
authors.forename: Pim
authors.initials: P
authors.lastname: Drinkenburg
authors.affiliation: a I-BioStat , Universiteit Hasselt , Diepenbeek , Belgium.|c Janssen Research & Development , Division of Janssen Pharmaceutica NV , Beerse , Belgium.
authors.forename: Helena
authors.initials: H
authors.lastname: Geys
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D015233:Models, Statistical; D015999:Multivariate Analysis; D051381:Rats; D012107:Research Design
keywords: Cluster analysis; EEG data; joint models; multivariate longitudinal data