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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20860

Title: A clustering algorithm for multivariate longitudinal data
Authors: Bruckers, Liesbeth
Molenberghs, Geert
Drinkenburg, P.
Geys, Helena
Issue Date: 2015
Citation: Journal of Biopharmaceutical Statistics, 26 (4), 725-741
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 paper 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 EEG data set quantifying the effect of psychoactive compounds on the brain activity in rats.
Notes: Bruckers, L (reprint author), Univ Hasselt, I BioStat, Martelarenlaan 42, B-3500 Hasselt, Diepen Beek, Belgium. liesbeth.bruckers@uhasselt.be
URI: http://hdl.handle.net/1942/20860
DOI: 10.1080/10543406.2015.1052476
ISI #: 000377095800010
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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