RE: model validation
Dear Ethan,
External model evaluation consists of a comparison between the validation
dataset (your new dataset) and the predictions obtained by applying the model
built on the learning dataset.
An usual way to do that is to perform Visual Predictive Checks (VPC), which
graphically compares the observations with their predictive distribution
according to the model.
Others technics, as NPDE could be applyied too for external model evaluation.
An add-on package for the open source statistical package R, designed to
computed NPDE, is available at www.npde.biostat.fr
http://www.npde.biostat.fr/ .
Best regards.
Karl
Refs:
N.H. Holford. The visual Predictive Check-Superiority to standard diagnostic
(Rorschach) plots. PAGE 14 (2005) Abstr 738
[www.page-meeting.org/?abstract=738].
M. O. Karlsson, N. H. Holford. A tutorial on visual predictive checks. PAGE 17
(2008) Abstr 1434 [www.page-meeting.org/?abstract=1434].
K. Brendel, E. Comets, C. Laffont, C. Laveille, F. Mentré. Metrics for external
model evaluation with an application to the population pharmacokinetics of
gliclazide. Pharm. Res. 23:2036-2049 (2006).
E. Comets, K. Brendel, F. Mentré. Computing normalised prediction distribution
errors to evaluate nonlinear mixed-effect models: the npde add-on package for
R. Comput. Methods Programs Biomed. 90:154-166 (2008).
________________________________
Quoted reply history
De : [email protected] [mailto:[email protected]] De la
part de Ethan Wu
Envoyé : jeudi 11 décembre 2008 18:42
À : [email protected]
Objet : [NMusers] model validation
Dear all,
First of all, as new comer to the list, I would like to say Hi to everyone.
I am planning to validate an PK model with a new dataset, my question is
that, what is the basic steps of using NONMEM to accomplish it? Many thanks in
advance.