A clustering algorithm for multivariate longitudinal data. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Bruckers, Liesbeth; Molenberghs, Geert; Drinkenburg, Pim; Geys, Helena
• Is Part Of: Journal of biopharmaceutical statistics. Volume 26:Issue 4(2016)
• Page Start: 725
• Page End: 741
• Publication Date: 2016-07-03
• Subjects: Cluster analysis -- EEG data -- joint models -- multivariate longitudinal data; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2015.1052476
• Languages: English
• ISSNs: 1054-3406

Abstract

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.