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.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Fieuws S,Verbeke G,Molenberghs Gdoi
10.1177/0962280206075305subject
Has Abstractpub_date
2007-10-01 00:00:00pages
387-97issue
5eissn
0962-2802issn
1477-0334pii
0962280206075305journal_volume
16pub_type
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