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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4011

Title: Random-effects models for multivariate repeated measures
Authors: Fieuws, Steffen
Verbeke, Geert
Molenberghs, Geert
Issue Date: 2007
Abstract: Mixed models are widely used for the analysis of one repeatedly measured outcome. If more than one outcome is present, a mixed model can be used for each one. These separate models can be tied together into a multivariate mixed model by specifying a joint distribution for their random effects. This strategy has been used for joining multivariate longitudinal profiles or other types of multivariate repeated data. However, computational problems are likely to occur when the number of outcomes increases. A pairwise modeling approach, in which all possible bivariate mixed models are fitted and where inference follows from pseudo-likelihood arguments, has been proposed to circumvent the dimensional limitations in multivariate mixed models. An analysis on 22-variate longitudinal measurements of hearing thresholds illustrates the performance of the pairwise approach in the context of multivariate linear mixed models. For generalized linear mixed models, a data set containing repeated measurements of seven aspects of psycho-cognitive functioning will be analyzed.
Notes: Katholieke Univ Leuven, Ctr Biostat, UZ St Rafael, B-3000 Louvain, Belgium. Hasselt Univ, Ctr Stat, Diepenbeek, Belgium.VERBEKE, G, Katholieke Univ Leuven, Ctr Biostat, UZ St Rafael, Kapucijnenvoer 35, B-3000 Louvain, Belgium.geert.verbeke@med.kuleuven.be
URI: http://hdl.handle.net/1942/4011
Link to publication: https://www.researchgate.net/publication/6183286_Random-effects_model_for_multivariate_repeated_measures
DOI: 10.1177/0962280206075305
ISI #: 000250441300002
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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