Diagnosing Model Diagnostics

By M. O. Karlsson, R. Savic. Clinical pharmacology and therapy, 2007

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Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions (2009, background): “Although the advantages and strengths of graphical diagnostics have been justified and stressed on several occasions, shortcomings have not been systematically explored (10).”
Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models (2011, methodology): “Model parameters were estimated based on three models: [1] the true model, [ 2 ] a model that assumed no allometric scaling, and [3] a model assumed no allometric scaling but with characterized correlation between Vmax and V. A fourth model, with BSV for all parameters assumed to be twice as high as”
Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics (2017, methodology): “relation between EBEs and covariates (Supplementary Figure S6b).(7,10) If overfitting occurs, IWRES will shrink toward 0, which makes model evaluation based on this metric less effective or informative.”
Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: The npde add-on package for R (2008, unknown): “[30] M.”