NONMEM Users Network Archive

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Re: RE: VPC, NPC or PPC?

From: saik.urien.svp <saik.urien>
Date: Thu, 26 Feb 2009 10:39:19 +0100

Hello

Regarding the NPDE metrics, at the last PAGE we had a discussion with France
Mentré, Emmanuelle Comets and Karl Brendel and agreed that it should be
juged on
- the visual aspect (distribution of npde)
-the mean is not significantly different from 0
-and the variance is not significantly different from 1

However, I experienced a non negligible number of model fittings for which
the normality test was OK

Perhaps France will further comment on this

Saïk

Dr Saik URIEN
Directeur de Recherche à l' INSERM
C.I.C. Mère-enfant Cochin-Necker
E.A.3620 - Université Paris Descartes
Unité de Recherche Clinique (URC)
HOPITAL TARNIER
89 rue d'Assas
F75006 PARIS

01 5841 2880
Email : saik.urien



----- Original Message -----
From: Stephen Duffull <stephen.duffull
To: "SIMON Nicolas" <nicolas.simon
Sent: Wednesday, February 25, 2009 7:33 PM
Subject: [NMusers] RE: VPC, NPC or PPC?


> Nicolas
>
> > We have a dataset with as many dosing (amount and length of
> > infusion) as patients. Once the final model was defined, I
> > have performed a vpc. However, because the dosing are very
> > different between patients, is it relevant to perform vpc or
> > shall we compute npc or ppc?
> >
> >
> >
> > Can somebody explain the basic difference between vpc, npc
> > and ppc and when shall we used one or the other?
> >
>
> Despite how it sounds this is not really a simple question.
>
> Mostly the purpose of all of these techniques is to assess how well the
model describes the data. This can be achieved visually and numerically.
If you want your method to have "diagnostic" properties, i.e. an ability to
determine where the model may fail, then visual types of checks tend to be
more informative. Numerical types of checks really give you an overall
feeling of whether your model fits the data but don't often allow you to
determine where in particular the model might fail.
>
> Numerical checks include PPC and NPDE (and others). PPC is really a
Bayesian construct as it checks the posterior distributions of your
parameters and hence isn't naturally something that would be performed in an
MLE framework. However there have been many examples where PPCs have been
performed in NONMEM (publications on this have appeared in JPKPD). PPC is
generally used to test a specific (important) feature of the data (hence is
generally not diagnostic for the whole model). NPDE provides a more general
numerical description of agreement of model and data, but when the statistic
is tested it seems to reject most model (hence is not diagnostic).
>
> Despite the apparent division into visual and numerical there is no reason
why a "VPC" couldn't be expressed numerically as a numerical predictive
check and why PPC or NPDE style techniques could not be expressed
graphically. We have recently produced examples of visual versions of PPC
as a form of visual predictive check for situations similar to what you have
described where traditional VPCs don't work well (note we did this in
WinBUGS).
>
> Steve
> --
> Professor Stephen Duffull
> Chair of Clinical Pharmacy
> School of Pharmacy
> University of Otago
> PO Box 913 Dunedin
> New Zealand
> E: stephen.duffull
> P: +64 3 479 5044
> F: +64 3 479 7034
>
> Design software: www.winpopt.com
>
>
>
Received on Thu Feb 26 2009 - 04:39:19 EST

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