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B1558
Title: Modeling the ARMA random effects covariance matrix in logistic random effects models Authors:  Keunbaik Lee - Sungkyunkwan University (Korea, South) [presenting]
Abstract: Logistic random effects models (LREMs) have been frequently used to analyze longitudinal binary data. When using a random effects covariance matrix to make proper inferences on covariate effects, the random effects in the models account for both within-subject association and between-subject variation. Estimation of the covariance matrix is challenging, however, because it is high-dimensional and should be positive definite. To overcome these limitations, two Cholesky decomposition approaches were proposed for precision matrix and covariance matrix: modified Cholesky decomposition and moving average Cholesky decomposition, respectively. When there are non-trivial and complicated correlations of repeated outcomes, however, the two approaches may not work. We combine the two decomposition approaches to model the random effects covariance matrix in the LREMs, thereby capturing a wider class of sophisticated dependence structure while achieving parsimony in parametrization. We then analyzed lung cancer data using our proposed model.