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Article

A Shared Parameter Model for Longitudinal Data with Missing Values

1Department of Statistics, Faculty of Economics and Political Science, Cairo University, Cairo, Egypt


American Journal of Applied Mathematics and Statistics. 2013, Vol. 1 No. 2, 30-35
DOI: 10.12691/ajams-1-2-3
Copyright © 2013 Science and Education Publishing

Cite this paper:
Ahmed M. Gad, Nesma M. M. Darwish. A Shared Parameter Model for Longitudinal Data with Missing Values. American Journal of Applied Mathematics and Statistics. 2013; 1(2):30-35. doi: 10.12691/ajams-1-2-3.

Correspondence to: Ahmed M. Gad, Department of Statistics, Faculty of Economics and Political Science, Cairo University, Cairo, Egypt. Email: ahmed.gad@feps.edu.eg

Abstract

Longitudinal studies represent one of the principal research strategies employed in medical and social research. These studies are the most appropriate for studying individual change over time. The prematurely withdrawal of some subjects from the study (dropout) is termed nonrandom when the probability of missingness depends on the missing value. Nonrandom dropout is common phenomenon associated with longitudinal data and it complicates statistical inference. The shared parameter model is used to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM algorithm is developed to obtain the model parameter estimates. Also, parameter estimates of the dropout model have been obtained. Standard errors of estimates have been calculated using the developed Monte Carlo method. The proposed approach performance is evaluated through a simulation study. Also, the proposed approach is applied to a real data set.

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