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Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles
Author(s) -
Fieuws Steffen,
Verbeke Geert
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00507.x
Subject(s) - pairwise comparison , random effects model , bivariate analysis , generalized linear mixed model , mixed model , covariance , linear model , multivariate statistics , inference , mathematics , statistics , dimension (graph theory) , covariance matrix , computer science , econometrics , artificial intelligence , medicine , meta analysis , pure mathematics
Summary A mixed model is a flexible tool for joint modeling purposes, especially when the gathered data are unbalanced. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes and/or the number of used random effects per outcome increases. We propose a pairwise approach in which all possible bivariate models are fitted, and where inference follows from pseudo‐likelihood arguments. The approach is applicable for linear, generalized linear, and nonlinear mixed models, or for combinations of these. The methodology will be illustrated for linear mixed models in the analysis of 22‐dimensional, highly unbalanced, longitudinal profiles of hearing thresholds.