Marginalized random effects models for multivariate longitudinal binary data.
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.
Lee
Keunbaik
K
Biostatistics Program, School of Public Health, Louisiana State University Health Science Center, New Orleans, LA 70112, USA.
Joo
Yongsung
Y
Yoo
Jae Keun
JK
Lee
JungBok
J
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