Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

Baojiang Chen, Xiao Hua Zhou

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation-maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation, which gives consistent estimators if the missing data and response models are correctly specified; however, we do not need to specify the distribution of the covariates that have missing values. In this article, we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are MAR. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations.

Original languageEnglish (US)
Pages (from-to)830-842
Number of pages13
JournalBiometrics
Volume67
Issue number3
DOIs
StatePublished - Sep 2011
Externally publishedYes

Keywords

  • Doubly robust
  • Estimating equation
  • Missing at random
  • Missing covariate
  • Missing response

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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