Marginalized random effects models for multivariate longitudinal binary data
- Authors
- Lee, Keunbaik; Joo, Yongsung; Yoo, Jae Keun; Lee, 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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