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.
Highlights
We discuss the result of simulation study that compares the three techniques namely; Multiple Imputation Generalized Estimating Equations (MIGEE), Inverse Probability Weighted Generalized Estimating Equations (IPWGEE) and Double Robust Generalized Estimating Equations (DRGEE) for different sample size and different missingness rates on the response variable
Note that the primary focus was to compare MIGEE, IPWGEE and DRGEE, but we extend the results to include those obtained from full datasets using standard Generalized Estimating Equations (GEE)
Comparing MIGEE and DRGEE, it can be seen that DRGEE produces better estimates in terms of bias than MIGEE, except for β1, β2 ( N = 100 ) and β01, β02 ( N = 500 )
Summary
These are called repeated measures or longitudinal studies. Ordinal responses are regularly experienced in these studies It is exceptionally common for sets of longitudinal studies to be incomplete, in the sense that not all intended measurements of a subject outcome vector are observed. This turns the statistical analysis into a missing data problem. A number of issues arise in the analysis: 1) the issue of bias due to systematic differences between the observed measurements and unobserved data, 2) loss of efficiency and 3) complications in data handling and statistical inferences [1]
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