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A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations


Repeated-Measures longitudinal data is common in drug research, where every patient is repeatedly measured across time. Responses could either be continuous variables such as blood pressure or binary variables such as drug test positive/negative. Issues to be addressed are within-subject observation dependence, as well as the between-subject differences (mixed effects). Another important problem to address is the missingness and dropouts.

Full likelihood-based models such as the generalized linear mixed model (GLMM) together with EM algorithm could be utilized, given simplified parametric correlation structure between random components. If the interest is only the mean parameters, little in subject effects, the non-likelihood-based generalized estimating equations (GEE) is a good alternative. GEE circumvents the structural and computational complexities of likelihood-based models, and it is robust to misspecification of the working correlation structures of marginal observations. However, as a non-likelihood frequentist marginal model, GEE itself lacks strength dealing with missing data mechanism beyond missing completely at random (MCAR). Therefore, integration of multiple imputation and GEE (MI-GEE) is a great solution to longitudinal data with dropouts.

Real data application is performed on MI-GEE and GLMM. Our results for the bupropion study dataset show effective but not significant strength of Bupropion for treating methamphetamine dependence, which is consistent with previous studies and biological sense.

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