A Weighting Approach for GEE Analysis with Missing Data

Abstract

We propose a new weighting (WT) method to handle missing categorical outcomes in longitudinal data analysis using generalized estimating equations (GEE). The proposed WT provides a valid GEE estimator when the data are missing at random (MAR), and has more stable weights and shows advantage in efficiency compared to the inverse probability weighing method in the presence of small observation probabilities. The WT estimator is similar to the stabilized weighting (SWT) estimator under mild conditions, but it is more stable and efficient than SWT when the associations of the outcome with the observation probabilities and the covariate are strong.

Keywords:

• Generalized estimating equation (GEE)
• Imputation
• Inverse probability weighting (IPW)
• Longitudinal studies
• Missing at random (MAR)
• Stabilized weighting

Mathematics Subject Classification:

• 62F10
• 62F12

Acknowledgment

The authors are grateful to the referees for their helpful suggestions and comments.