For each observation, we define the prediction discrepancy as the percentile of this observation in the whole marginal predictive distribution under H 0.

Methods: The null hypothesis (H 0) is that data in the validation dataset can be described by a given model. We also evaluate the ability of NPDE to detect misspecification in the covariate model. We assess by simulation the statistical properties of NPDE for the evaluation of a population pharmacokinetic model in comparison with WRES. We developed a metric called Normalized Prediction Distribution Errors (NPDE) based on the whole predictive distribution, and we propose different tests and graphs. Among the different approaches proposed to evaluate a population model on an external validation dataset, standardised prediction errors (computed in NONMEM through WRES) are frequently used, but they are computed using a first-order approximation. We have also shown in a recent literature survey that model evaluation was not appropriately performed in most published population pharmacokinetic-pharmacodynamic analyses. Authors Brendel K (1,2), Comets E (1), Laffont CM (2), MentrĪffiliations INSERM U738, Paris, France University Paris 7, Paris, France (1), Institut de recherches internationales Servier, Courbevoie, France (2), AP-HP, Bichat Hospital, Paris, France (3)Ĭontext: Although population pharmacokinetic and/or pharmacodynamic model evaluation is highly recommended by regulatory authorities, there is no consensus today on the appropriate approach to assess a population model.

External validation nonmem