TY - DATA T1 - Dataset for: Modeling the random effects covariance matrix for longitudinal data with covariates measurement error PY - 2018/08/07 AU - Erfanul Hoque AU - Mahmoud Torabi UR - https://wiley.figshare.com/articles/dataset/Dataset_for_Modeling_the_random_effects_covariance_matrix_for_longitudinal_data_with_covariates_measurement_error/6650921 DO - 10.6084/m9.figshare.6650921.v1 L4 - https://ndownloader.figshare.com/files/12167615 L4 - https://ndownloader.figshare.com/files/12167663 L4 - https://ndownloader.figshare.com/files/12167666 L4 - https://ndownloader.figshare.com/files/12167669 L4 - https://ndownloader.figshare.com/files/12167672 L4 - https://ndownloader.figshare.com/files/12167675 L4 - https://ndownloader.figshare.com/files/12167678 L4 - https://ndownloader.figshare.com/files/12167681 L4 - https://ndownloader.figshare.com/files/12167684 L4 - https://ndownloader.figshare.com/files/12167687 L4 - https://ndownloader.figshare.com/files/12167690 L4 - https://ndownloader.figshare.com/files/12167693 L4 - https://ndownloader.figshare.com/files/12167660 L4 - https://ndownloader.figshare.com/files/12167657 L4 - https://ndownloader.figshare.com/files/12167621 L4 - https://ndownloader.figshare.com/files/12167624 L4 - https://ndownloader.figshare.com/files/12167627 L4 - https://ndownloader.figshare.com/files/12167633 L4 - https://ndownloader.figshare.com/files/12167636 L4 - https://ndownloader.figshare.com/files/12167639 L4 - https://ndownloader.figshare.com/files/12167642 L4 - https://ndownloader.figshare.com/files/12167645 L4 - https://ndownloader.figshare.com/files/12167648 L4 - https://ndownloader.figshare.com/files/12167651 L4 - https://ndownloader.figshare.com/files/12167654 L4 - https://ndownloader.figshare.com/files/12167696 KW - Cholesky decomposition KW - Longitudinal data KW - Measurement error KW - Monte Carlo Expectation-maximization algorithm KW - Random effects KW - Statistics KW - Medicine N2 - Longitudinal data occur frequently in practice such as medical studies and life sciences. Generalized linear mixed models (GLMMs) are commonly used to analyze such data. It is typically assumed that the random effects covariance matrix is constant across the subject (and among subjects) in these models. In many situations, however, this correlation structure may differ among subjects and ignore this heterogeneity can cause the biased estimate of model parameters. Recently, Lee et al. (2012) developed a heterogeneous random effects covariance matrix for GLMMs for error-free covariates. Covariates measured with an error also happen frequently in the longitudinal data set-up (e.g., blood pressure, cholesterol level). Ignoring this issue in the data may produce bias in model parameters estimate and lead to wrong conclusions. In this paper, we propose an approach to properly model the random effects covariance matrix based on covariates in the class of GLMMs where we also have covariates measured with error. The resulting parameters from the decomposition of random effects covariance matrix have a sensible interpretation and can easily be modeled without the concern of positive definiteness of the resulting estimator. Performance of the proposed approach is evaluated through simulation studies which show that the proposed method performs very well in terms of bias, mean squared error, and coverage rate. An application of the proposed method is also provided using a longitudinal data from Manitoba Follow-up study. ER -