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Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models |
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| Authors: | Keunbaik Lee Sanggil Kang Xuefeng Liu Daekwan Seo |
| Institution: | 1. Louisiana State University Health Sciences Center , New Orleans , LA , 70122 , USA;2. Department of Data Information , Sangji University , Wonju , Korea;3. Department of Biostatistics and Epidemiology , College of Public Health, East Tennessee State University , Johnson City , TN , 37614 , USA;4. Laboratory of Experimental Carcinogenesis , NCI/NIH Bethesda , MD , 20892 , USA |
| Abstract: | 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. |
| Keywords: | likelihood-based model random effects marginal model Quasi-Newton Kronecker product |