Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random

Baojiang Chen, Grace Y. Yi, Richard J. Cook

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing-data indicators. Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing, or both response and covariate data may be missing. Here we show that it is important to take the association between the missing data indicators for these two processes into account through joint models. Inverse probability-weighted generalized estimating equations offer an appealing approach for doing this. Here we develop these equations for a particular model generating intermittently missing-at-random data. Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples. Supplemental materials are available online.

Original languageEnglish (US)
Pages (from-to)336-353
Number of pages18
JournalJournal of the American Statistical Association
Issue number489
StatePublished - Mar 2010


  • Generalized estimating equation
  • Inverse probability weight
  • Joint model
  • Longitudinal data
  • Missing covariate
  • Missing response

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Weighted generalized estimating functions for longitudinal response and covariate data that are missing at random'. Together they form a unique fingerprint.

Cite this