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

Document Type : Original Paper

Author

Department of Statistics, Faculty of Science, University of Qom, Qom, Iran.

Abstract

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.

Keywords

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References

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Journal of Advanced Mathematical Modeling
Volume 7, Issue 2
April 2018
Pages 25-41

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History

  • Receive Date: 16 August 2017
  • Revise Date: 17 March 2018
  • Accept Date: 03 February 2018

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