Maximum Likelihood Approach for Longitudinal Models with Nonignorable Missing Data Mechanism Using Fractional Imputation

Abdallah S. A. Yaseen¹, Ahmed M. Gad^2, and Abeer S. Ahmed¹

¹The National Centre for Social and Criminological Research, Cairo, Egypt

²Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt

Cite this paper

Abdallah S. A. Yaseen, Ahmed M. Gad and Abeer S. Ahmed. Maximum Likelihood Approach for Longitudinal Models with Nonignorable Missing Data Mechanism Using Fractional Imputation. American Journal of Applied Mathematics and Statistics. 2016; 4(3):59-66. doi: 10.12691/AJAMS-4-3-1

Abstract

In longitudinal studies data are collected for the same set of units for two or more occasions. This is in contrast to cross-sectional studies where a single outcome is measured for each individual. Some intended measurements might not be available for some units resulting in a missing data setting. When the probability of missing depends on the missing values, missing mechanism is termed nonrandom. One common type of the missing patterns is the dropout where the missing values never followed by an observed value. In nonrandom dropout, missing data mechanism must be included in the analysis to get unbiased estimates. The parametric fractional imputation method is proposed to handle the missingness problem in longitudinal studies and to get unbiased estimates in the presence of nonrandom dropout mechanism. Also, in this setting the jackknife replication method is used to find the standard errors for the fractionally imputed estimates. Finally, the proposed method is applied to a real data (mastitis data) in addition to a simulation study.

Keywords

longitudinal data, mastitis data, missing data, nonrandom dropout, parametric fractional imputation, repeated measures, standard errors

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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