Modeling Mixed Continuous and Ordinal Longitudinal Responses Under Drop-out Mechanism

In some longitudinal studies, especially in social, economic, medical and other fields, there may be two interested responses with two different scales at a time where they may be correlated with each other. Also, considering the nature of longitudinal studies, each of the responses associated with a subject over time can also be correlated. So two correlation structure should be considered simultaneously in the data analysis. In a longitudinal study, some subjects may not be available for any reason (such as displacement, death and others),
In a longitudinal study, some subjects may withdraw for any reason (such as displacement, death, etc.) and their information is not available. In this case, joint modeling of longitudinal data and drop-out event is more desirable than separate modeling of either one. In this paper, the mathematics modeling of this type of data under drop-out mechanism is presented using Bayesian approach. A Simulations study and a real data analysis is used to evaluate the performance of the proposed model. This model includes the presented models for complete data as a special case when there is no drop-out in the data set. Also, some tests for choosing the best fitted model to data are performed in the real data analysis.