Marginalized random effects models for multivariate longitudinal binary data

Keunbaik Lee, Yongsung Joo, Jae Keun Yoo, Jung Bok Lee

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish
Pages (from-to)1284-1300
Number of pages17
JournalStatistics in Medicine
Volume28
Issue number8
DOIs
StatePublished - 15 Apr 2009

Keywords

  • Cohort study
  • Marginalized models
  • Multivariate longitudinal data

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