Joint GEEs for multivariate correlated data with incomplete binary outcomes

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-eff...

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Bibliographic Details
Published in:Journal of applied statistics 2017-08, Vol.44 (11), p.1920-1937
Main Authors: Inan, G., Yucel, R.
Format: Article
Language:English
Subjects:
62-07
Computer simulation
Incomplete binary responses
Kronecker product correlation matrix
MAR
marginal models
Mathematical models
Monte Carlo simulation
multiple imputation
Multivariate analysis
Regression analysis
Regression coefficients
rounding
Statistical methods
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Summary: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.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2016.1238049