a model for multivariate longitudinal rank data with application to glioma patients

This paper proposes a model to analyze longitudinal rank responses using a bayesian approach with a random effects framework‎. ‎we consider rank responses that are implicitly determined by their latent variables‎. ‎further‎, ‎the usual univariate model‎, ‎as well as a multivariate model‎, ‎is also considered for analyzing the multiple longitudinal rank responses‎. ‎we use random effect vectors to evaluate the correlation between individual responses across time‎. ‎also‎, ‎a bayesian approach that is used to yield bayesian estimates of the model's parameters‎. ‎some simulation studies are conducted to estimate the parameters of the considered models‎. ‎the model is used for a neurocognitive data set of glioma patients who underwent surgery‎. ‎the results of the data analysis are presented to illustrate the method.