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Marginalized random effects models for multivariate longitudinal binary data

Authors
Lee, KeunbaikJoo, YongsungYoo, Jae KeunLee, JungBok
Issue Date
15-Apr-2009
Publisher
WILEY
Keywords
multivariate longitudinal data; marginalized models; Cohort Study
Citation
STATISTICS IN MEDICINE, v.28, no.8, pp.1284 - 1300
Indexed
SCIE
SCOPUS
Journal Title
STATISTICS IN MEDICINE
Volume
28
Number
8
Start Page
1284
End Page
1300
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/120239
DOI
10.1002/sim.3534
ISSN
0277-6715
Abstract
Generalized linear models with random effects are often used to explain the serial dependence of longitudinal categorical data. Marginalized random effects models (MREMs) permit likelihood-based estimations of marginal mean parameters and also explain the serial dependence of longitudinal data. In this paper, we extend the MREM to accommodate multivariate longitudinal binary data using a new covariance matrix with a Kronecker decomposition, which easily explains both the serial dependence and time-specific response correlation. A maximum marginal likelihood estimation is proposed utilizing a quasi-Newton algorithm with quasi-Monte Carlo integration of the random effects. Our approach is applied to analyze metabolic syndrome data from the Korean Genomic Epidemiology Study for Korean adults. Copyright (C) 2009 John Wiley & Sons, Ltd.
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