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Methods and Applications of Longitudinal Data Analysis by Xian Liu

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8.4. Nonlinear predictions and retransformation of random components

Given a variety of approximation methods in the analysis of nonlinear longitudinal data, the fixed effects and the covariance parameters in GLMMs can be derived either from the linearization approaches or from the integral approximation methods. In displaying results of various nonlinear longitudinal models, it is often not sufficient to produce meaningful interpretations just by presenting parameter estimates and the corresponding standard errors. For example, in the presence of more than two nominal response outcomes, the regression coefficients of covariates do not necessarily bear any relationship to changes in the multinomial response (Greene, 2003; Liao, 1994; Liu et al., ...

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