A weighting approach for GEE analysis with missing data

Cuiling Wang, Myunghee Cho Paik

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2397-2411
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Issue number13
StatePublished - Jan 1 2011


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

ASJC Scopus subject areas

  • Statistics and Probability


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