Background
npde is a metrics designed to evaluate non-linear mixed effect models. Model evaluation is an important part of model building. Since the development of statistical software allowing parameter estimation in non-linear mixed effect models in the early 70's, model evaluation and diagnostic graphs have mostly used standardised prediction errors (a.k.a. weighted residuals or WRES in the software NONMEM), but this metric is computed using a first-order linearisation of the model function and as such, its shortcomings have been amply characterised, most notably an increase in the type I error when comparing models or building covariate models [8,9]. Prediction discrepancies (pd) were proposed as an improvement over this previous metric: they take into account the full predictive distribution of each observation [1]. Normalised prediction distribution errors (npde) further improved over pd by taking into account the correlation between multiple observations in each subject [2].
New recent developments in npde include: methods for handling BQL data [4], graphs with prediction bands available for standard VPC (visual predictive check), pd/npde versus time or predictions [5], and graphs to assess covariate models [6].
We designed the software npde to easily compute npde and pd.
npde was programmed as an add-on package for the R software [7]. The current version is 3.0, including the developments in [4-6]
Documentation is available in the relevant section and includes detailed explanations and examples as to how to use npde.
Main references
[1] Comets E, Mentré F. Developing tools to evaluate non-linear mixed effect models : 20 years on the npde adventure. AAPS Journal, 2021, 23: 75. Link to PDF of paper
[2] Cerou M, Lavielle M, Brendel K, Chenel M, Comets E. Development and performance of npde for the evaluation of time-to-event models, Journal of Pharmacokinetics and Biopharmaceutics, 2006, 33: 345-67. Link to PDF of paper
[3] Nguyen THT, Mouksassi MS, Holford N, Al-Huniti N, Freedman I, Hooker AC, John J, Karlsson MO, Mould DR, Ruixo JJP, Plan EL, Savic R, van Hasselt JG, Weber B, Zhou C, Comets E, Mentré F, the Model Evaluation Group of the International Society of Pharmacometrics (ISoP) Best Practice Committee. Model evaluation of continuous data pharmacometric models : metrics and graphics, Clinical Pharmacology and Therapeutics : Pharmacometrics & Systems Pharmacology, 2017, 6: 87–109. Link to PDF of paper
[4] Nguyen TH, Comets E, Mentré F. Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model, Journal of Pharmacokinetics and Pharmacodynamics, 2012, 39:499-518. Link to abstract
[5] Comets E, Brendel K, Mentré F. Model evaluation in nonlinear mixed effect models, with applications to pharmacokinetics, Journal de la Société Francaise de Statistiques, 2010, 151: 106-28. Link to PDF of paper
[6] Brendel K, Comets E, Laffont C, Mentré F. Evaluation of different tests based on observations for external model evaluation of population analyses, Journal of Pharmacokinetics and Pharmacodynamics, 2010, 37: 49-65. Link to PDF of paper
[7] Comets E, Brendel K, Mentré F. Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R, Computer Methods and Programs in Biomedicine, 2008, 90: 154-66. Link to PDF of paper
[8] Mentré F, Escolano S. Prediction discrepancies for the evaluation of nonlinear mixed-effects models, Journal of Pharmacokinetics and Biopharmaceutics, 2006, 33: 345-67. Link to PDF of paper
[9] Brendel K, Comets E, Laffont C, Laveille C, Mentré F. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide, Pharmaceutical Research, 2006, 23: 2036-49. Link to PDF of paper
See also references in the reference page.
Contact information
npde is maintained by Emmanuelle Comets (emmanuelle.comets@inserm.fr) Inserm, IAME UMR 1137, Paris, France. Please address any questions, bug notice or suggestions.
Licence
npde is a software distributed under the terms of the GNU GENERAL PUBLIC LICENSE Version 2, June 1991. The terms of this license are in a file called COPYING found in the library.
Citations
When using npde in a scientific publication, please use the following citation to reference the software:
Comets E, Brendel K, Mentré F. Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R, Computer Methods and Programs in Biomedicine, 2008, 90: 154-66.
