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Marginalized models for longitudinal ordinal data with nonignorable dropout
Journal of the Korean Data & Information Science Society 2019;30:479-90
Published online March 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.2.479
© 2019 Korean Data and Information Science Society.

Keunbaik Lee1

1Department of Statistics, Sungkyunkwan University
Correspondence to: Associate professor, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea. E-mail: keunbaik@skku.edu
This project was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology (NRF- 2016R1D1A1B03930343).
Received February 13, 2019; Revised March 5, 2019; Accepted March 5, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In longitudinal studies, missing data were commonly occurred. Since the missing data result in biased estimates of the estimated effects of covariates, they must be considered carefully. In this paper, we consider a selection model approach to deal with the missing data in longitudinal studies. Especially, we analyze longitudinal ordinal data with missing not at random dropouts using joint models of marginalized models and selection models. The proposed methods are illustrated using data from a longitudinal cancer clinical trial.
Keywords : Fisher-scoring, generalized linear models, Markov structure, missing not at random, random e ects.