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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 | Source: | 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 | Keywords: | cluster analysis; EEG data; joint models; multivariate longitudinal data | Document URI: | http://hdl.handle.net/1942/20860 | ISSN: | 1054-3406 | e-ISSN: | 1520-5711 | DOI: | 10.1080/10543406.2015.1052476 | ISI #: | 000377095800010 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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467.pdf | Peer-reviewed author version | 17.05 MB | Adobe PDF | View/Open |
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