Abstract
In order to analyze longitudinal ordinal data, researchers commonly use the cumulative logit random effects model. In these models, the random effects covariance matrix is used to account for both subject variation and serial correlation of repeated outcomes. However, the covariance matrix is assumed to be homoscedastic and restricted due to the high-dimensionality and positive-definiteness of the matrix. In order to relieve these assumptions, three Cholesky decomposition methods were proposed to model the random effects covariance matrix: modified Cholesky, moving average Cholesky, and autoregressive moving-average decompositions. We also use the three decompositions to model the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. In addition, Bayesian methods are presented for the parameter estimation of the proposed models, and Markov Chain Monte Carlo is conducted using the JAGS program. The proposed methods are illustrated using lung cancer data.
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This work was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology (NRF-2016R1D1A1B03930343).
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This paper is based on part of Jiyeong Kim’s PhD thesis.
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Kim, J., Sohn, I. & Lee, K. Bayesian cumulative logit random effects models with ARMA random effects covariance matrix. J. Korean Stat. Soc. 49, 32–54 (2020). https://doi.org/10.1007/s42952-019-00003-1
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DOI: https://doi.org/10.1007/s42952-019-00003-1