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

Authors

  • Baojiang Chen,

    Corresponding author
    1. Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, U.S.A.
      email: baojiang.chen@unmc.edu
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  • Xiao-Hua Zhou

    Corresponding author
    1. Northwest HSR&D Center of Excellence, Department of Veterans Affairs Seattle Medical Center, Seattle, Washington 98101, U.S.A.
      email: baojiang.chen@unmc.edu
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email:baojiang.chen@unmc.edu

email:azhou@u.washington.edu

Abstract

Summary 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.

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