Case deletion diagnostic

There are alternative criteria besides the prediction power to examine models. These are case deletion diagnostics [17,21], which are used to deter-... 

When a model is used for descriptive purposes, goodness-of-ht, reliability, and stability, the components of model evaluation must be assessed. Model evaluation should be done in a manner consistent with the intended application of the PM model. The reliability of the analysis results can be checked by carefully examining diagnostic plots, key parameter estimates, standard errors, case deletion diagnostics (7-9), and/or sensitivity analysis as may seem appropriate. Conhdence intervals (standard errors) for parameters may be checked using nonparametric techniques, such as the jackknife and bootstrapping, or the prohle likelihood method. Model stability to determine whether the covariates in the PM model are those that should be tested for inclusion in the model can be checked using the bootstrap (9). 

Data structure analysis is the examination of the raw data for hidden structure, outliers, or leverage observations. This is repeated during the exploratory modeling (and nonlinear mixed effects modeling) steps using case deletion diagnostics (20). This type of analysis is important since outliers or leverage observations may occur in a population PM data set. It is equally important for the reduction of the covariate vector. 

Similar to the linear regression case, influence diagnostics for linear mixed models can be developed using case deletion. These extensions include Cook s distance, DFFITs, COVRATIO, and PRESS residuals. For example, a linear mixed effects extension to DFFITS is... 

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