Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models
Likelihood-based marginalized models using random effects have become popular for analyzing longitudinal categorical data. These models permit direct interpretation of marginal mean parameters and characterize the serial dependence of longitudinal outcomes using random effects [12,22].
In this paper, we propose model that expands the use of previous models to accommodate longitudinal nominal data. Random effects using a new covariance matrix with a Kronecker product composition are used to explain serial and categorical dependence. The Quasi-Newton algorithm is developed
for estimation. These proposed methods are illustrated with a real data set and compared with other standard methods.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Louisiana State University Health Sciences Center, New OrleansLA70122, USA
Department of Data Information,Sangji University, Wonju, Korea
Department of Biostatistics and Epidemiology,College of Public Health, East Tennessee State University, Johnson CityTN37614, USA
Laboratory of Experimental Carcinogenesis,NCI/NIH Bethesda, MD20892, USA
August 1, 2011