Marginal methods for clustered longitudinal binary data with incomplete covariates

Baojiang Chen, Grace Y. Yi, Richard J. Cook, Xiao Hua Zhou

Research output: Contribution to journalArticle


Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.

Original languageEnglish (US)
Pages (from-to)2819-2831
Number of pages13
JournalJournal of Statistical Planning and Inference
Issue number10
Publication statusPublished - Oct 1 2012



  • Association
  • Generalized estimating equation
  • Longitudinal data
  • Missing covariates

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this