RE: [NMusers] Predictive Performance

Dear Navin,

As Juergen said, VPC is a good graphically tool to indicate a clear bias =
in model prediction. One of the limitation of this approach may be when =
you have a complex design with diferent doses, different administration =
schedules and different covariates (implemented in your population =
model). In this case you need to perform several VPC plots splitted by =
doses, covariates...

Another approach is to compute a metric called Normalized Predictive =
Distribution Error (NPDE). NPDE* have been developed to take into =
account the full predictive distribution of each individual =
concentration, and handle multiple observations within subjects. Under =
the null hypothesis that a model under scrutiny describes a validation =
dataset, the distribution of NPDE should be the standard normal =
distribution.

A R package is now available and can be downloaded from =
WWW.npde.biostat.fr .

Best regards.

Karl

Brendel K., Comets E., Laffont C., Laveille C., Mentré F. Metrics for =
external model evaluation with an application to the population =
pharmacokinetics of gliclazide. Pharm Res 2006, 23:2036-2049.



-----Message d'origine-----
De : owner-nmusers_at_globomaxnm.com
[mailto:owner-nmusers_at_globomaxnm.com]De la part de Jurgen Bulitta
Envoyé : mardi 3 juillet 2007 02:07
À : navin goyal; nmusers_at_globomaxnm.com
Objet : Re: [NMusers] Predictive Performance


Dear Navin,

If you want to assess the predictive performance of a model,
I would highly recommend using visual predictive checks (VPC,
also called simple predictive checks, or degenerate predictive
checks).

Depending on your study design, VPCs might be easy to
implement or more work intensive. I find VPCs much easier
to interpret than DV vs. PRED or DV vs. IPRED plots. VPCs
are also easily communicated to non-modelers.

If the DV vs. IPRED plot looks biased, a model is often not
flexible enough to describe the data. However, there are
situations when the DV vs. IPRED plot looks almost perfect,
but the DV vs. PRED plot is quite biased and the VPC indicates
a clear bias in model predictions. This might be due to problems
with the parameter variability model.

So in essence, I would look at all three of those plots to assess
the appropriateness of a model. If a model is intended for
simulations, the VPC is a powerful tool to visually assess the
predictive performance and to tell if a potential bias in simulations
might be important for the study objectives or not.
Please find some references below.

Best regards
Juergen


Yano Y, Beal SL, Sheiner LB. Evaluating pharmacokinetic/pharmacodynamic =
models using the posterior predictive check.
J Pharmacokinet Pharmacodyn. 2001 Apr;28(2):171-92.

Mentre F, Escolano S. Prediction discrepancies for the evaluation of =
nonlinear mixed-effects models.
J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):345-67.


-----------------------------------------------
Juergen Bulitta, PhD, Post-doctoral Fellow
Pharmacometrics, University at Buffalo, NY, USA
Phone: +1 716 645 2855 ext. 281, j_at_bulitta.com
-----------------------------------------------






-----Ursprüngliche Nachricht-----
Von: "navin goyal" <navin1180_at_gmail.com>
Gesendet: 02.07.07 20:10:56
An: nmusers <nmusers_at_globomaxnm.com>
Betreff: [NMusers] Predictive Performance


Hi everybody,
I had a question about the Predictive performance of the POPPK Model.
When I am estimating the precision and bias with the POPPK model I have, =
am I supposed to use the
individual predictions or the population predictions ???

I am using "Some suggestions for Measuring Predictive Performance" by =
Sheiner and Beal : J Pk and Bio Vol (:(4) 1981 :503-512 as reference.


I guess I should be using the population predictions to calculate the =
precision and bias as I want to use the model to predict the plasma =
concentrations. Or does this choice depend on anything else ??
If I am using the Population predictions then, where else would I be =
using the individual Predictions apart from plotting them against the DV =
 to evaluate the Goodness of Fit?



Thanks in advance


--
--Navin

Received on Tue Jul 03 2007 - 04:28:05 EDT