A clustering algorithm for multivariate longitudinal data |
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Authors: | Liesbeth Bruckers Geert Molenberghs Pim Drinkenburg Helena Geys |
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Affiliation: | 1. I-BioStat, Universiteit Hasselt, Diepenbeek, Belgiumliesbeth.bruckers@uhasselt.be;3. I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium;4. I-BioStat, Katholieke Universiteit Leuven, Leuven, Belgium;5. Janssen Research &6. Development, Division of Janssen Pharmaceutica NV, Beerse, Belgium;7. Janssen Research & |
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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. |
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Keywords: | Cluster analysis EEG data joint models multivariate longitudinal data |
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