PURPOSE: Within-subject dependency of observations has a strong impact on the evaluation of population pharmacokinetic (PK) and/or pharmacodynamic (PD) models. To our knowledge, none of the current model evaluation tools correctly address this issue. We present a new method with a global test and easy diagnostic plot which relies on the use of a random projection technique that allows the analysis of dependent data. METHODS: For each subject, the vector of standardised residuals is calculated and projected onto many random directions drawn uniformly from the unit sphere. Our test compares the empirical distribution of projections with their distribution under the model. Simulation studies assess the level of the test and compare its performance with common metrics including normalised prediction distribution errors and different types of weighted residuals. An application to real data is performed. RESULTS: In contrast to other evaluated methods, our test shows adequate level for all models and designs investigated, which confirms its good theoretical properties. The weakness of other methods is demonstrated and discussed. CONCLUSIONS: This new test appears promising and could be used in combination with other tools to drive model evaluation in population PK/PD analyses.
PURPOSE: Within-subject dependency of observations has a strong impact on the evaluation of population pharmacokinetic (PK) and/or pharmacodynamic (PD) models. To our knowledge, none of the current model evaluation tools correctly address this issue. We present a new method with a global test and easy diagnostic plot which relies on the use of a random projection technique that allows the analysis of dependent data. METHODS: For each subject, the vector of standardised residuals is calculated and projected onto many random directions drawn uniformly from the unit sphere. Our test compares the empirical distribution of projections with their distribution under the model. Simulation studies assess the level of the test and compare its performance with common metrics including normalised prediction distribution errors and different types of weighted residuals. An application to real data is performed. RESULTS: In contrast to other evaluated methods, our test shows adequate level for all models and designs investigated, which confirms its good theoretical properties. The weakness of other methods is demonstrated and discussed. CONCLUSIONS: This new test appears promising and could be used in combination with other tools to drive model evaluation in population PK/PD analyses.
Authors: Liping Zhang; Vikram Sinha; S Thomas Forgue; Sophie Callies; Lan Ni; Richard Peck; Sandra R B Allerheiligen Journal: J Pharmacokinet Pharmacodyn Date: 2006-06-13 Impact factor: 2.745
Authors: T H T Nguyen; M-S Mouksassi; N Holford; N Al-Huniti; I Freedman; A C Hooker; J John; M O Karlsson; D R Mould; J J Pérez Ruixo; E L Plan; R Savic; J G C van Hasselt; B Weber; C Zhou; E Comets; F Mentré Journal: CPT Pharmacometrics Syst Pharmacol Date: 2017-02-10