Joint GEEs for multivariate correlated data with incomplete binary outcomes |
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Authors: | G. Inan R. Yucel |
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Affiliation: | 1. School of Statistics, University of Minnesota, Minneapolis, MN, USA;2. Department of Epidemiology and Biostatistics, University at Albany, Suny, NY, USA |
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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. |
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Keywords: | Incomplete binary responses MAR Kronecker product correlation matrix marginal models multiple imputation rounding |
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