TITLE:
A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts
AUTHORS:
Kago Edwin Ditlhong, Oscar Owino Ngesa, Abdalla Yusuf Kombo
KEYWORDS:
Longitudinal Ordinal Data, MAR, MCAR, Multiple Imputation GEE, Inverse Probability Weighted GEE, Double Robust GEE
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.5,
September
18,
2018
ABSTRACT: 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.