A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts — Oak Academic Publishing
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A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts
Pan African University Institute of Basic Sciences, Technology and Innovation, Nairobi, Kenya
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Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
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Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
1 Pan African University Institute of Basic Sciences, Technology and Innovation, Nairobi, Kenya
2 Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
3 Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
In longitudinal studies, measurements are taken repeatedly over time on the same experimental unit. These measurements are thus correlated. Missing data are very common in longitudinal studies. A lot of research has been going on ways to appropriately analyze such data set. Generalized Estimating Equations (GEE) is a popular method for the analysis of non-Gaussian longitudinal data. In the presence of missing data, GEE requires the strong assumption of missing completely at random (MCAR). Multiple Imputation Generalized Estimating Equations (MIGEE), Inverse Probability Weighted Generalized Estimating Equations (IPWGEE) and Double Robust Generalized Estimating Equations (DRGEE) have been proposed as elegant ways to ensure validity of the inference under missing at random (MAR). In this study, the three extensions of GEE are compared under various dropout rates and sample sizes through simulation studies. Under MAR and MCAR mechanism, the simulation results revealed better performance of DRGEE compared to IPWGEE and MIGEE. The optimum method was applied to real data set.
KeywordsLongitudinal Ordinal DataMARMCARMultiple Imputation GEEInverse Probability Weighted GEEDouble Robust GEE
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