A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts

• 作者: Abdalla Yusuf Kombo Kago Edwin Ditlhong Oscar Owino Ngesa
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Longitudinal ORDINAL Data MAR MCAR Multiple IMPUTATION GEE Inverse Probability Weighted GEE Double Robust GEE
• 年，卷（期）: 2018,（5）
• 页码范围: 770-792
• 页数: 23页
• 分类号: R73

摘要

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.