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Joint GEEs for multivariate correlated data with incomplete binary outcomes

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  • G. Inan
  • R. Yucel

Abstract

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.

Suggested Citation

  • G. Inan & R. Yucel, 2017. "Joint GEEs for multivariate correlated data with incomplete binary outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1920-1937, August.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1920-1937
    DOI: 10.1080/02664763.2016.1238049
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    References listed on IDEAS

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