Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling

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Innovative methods for modeling continuous and categorical correlated outcomes are presented, enhancing traditional approaches like generalized estimating equations (GEE) and linear mixed modeling. The book introduces partially modified GEE, which incorporates variance/dispersion parameters into standard GEE, and fully modified GEE, offering alternative estimating equations for both mean and variance parameters. Extended linear mixed modeling (ELMM) further utilizes the likelihood function for comprehensive parameter estimation. Detailed formulations for algorithmic solutions and empirical tests are also included.