title: Marginalized random effects models for multivariate longitudinal binary data.
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.
authors.affiliation: Biostatistics Program, School of Public Health, Louisiana State University Health Science Center, New Orleans, LA 70112, USA.
authors.forename: Keunbaik
authors.initials: K
authors.lastname: Lee
authors.forename: Yongsung
authors.initials: Y
authors.lastname: Joo
authors.forename: Jae Keun
authors.initials: JK
authors.lastname: Yoo
authors.forename: JungBok
authors.initials: J
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D024821:Metabolic Syndrome / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis