Determination of Appropriate Covariance Structures in Random Slope and Intercept Model Applied in Repeated Measures

Abstract

This study aims to determine variance-covariance structures of dependent variable in data set containing repeated measures and to compare covariance parameter estimation methods. To this end, random intercept and slope model which is among the special cases of linear mixed model was formed and the time variable was involved into the model in a continuous and categorical manner. Also, compound symmetry (CS), toeplitz (TOEP), first-order autoregressive (AR(1)), homogeneous variance-covariance models and unstructured (UN), heterogeneous compound symmetry (CSH), heterogeneous toeplitz (TOEPH), heterogeneous first-order autoregressive (ARH(1)), first-order ante-dependence (ANTE(1)) and unstructured correlation (UNR) heterogeneous variance-covariance models were performed in order to determine the variance-covariance structure between the repeated measures. In addition, comparison of ML and REML was carried out as covariance parameter estimation method. Consequently, random intercept and slope model (RISM) was found to be the most appropriate one in modeling the repeated measure data when ML was used as the parameter estimation method and UN, CSH, ARH(1), TOEPH, ANTE(1), UNR as the covariance models.