Dear Susan,
The cut function in S-plus is quite useful for binning. The cut function creates a category object by dividing continuous data into intervals. One can use the nominal (protocol) times as breakpoints and labels in the cut function. To read more about binning please see the abstract by Drs. Karlsson and Holford on VPC from this year's PAGE meeting.
http://www.page-meeting.org/?abstract=1434
Dr. Holford / Dr. Karlsson could you kindly post your presentation from this
year's PAGE VPC tutorial on their webpage?
Thanks...Mahesh
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Mouksassi
Mohamad-Samer
Sent: Thursday, July 24, 2008 11:54 AM
To: Willavize, Susan A; Nick Holford; [email protected]
Subject: RE: FW: [NMusers] PPC
Dear Susan,
Binning is to have sufficient number of points to compute quantiles of interest.
PSN. 2.2.5 has a predictive check utilities and very extensive options
regarding binning and stratifying. The description document may be useful to
understand more about binning.
For uncertainties you may use the bootstrap distribution or the asymptotic
distribution from a covariance step.
Kind Regards,
Samer
-----Original Message-----
From: [EMAIL PROTECTED] on behalf of Willavize, Susan A
Sent: Wed 7/23/2008 08:38
To: Nick Holford; [email protected]
Subject: RE: FW: [NMusers] PPC
Hi Nick,
I have been following this discussion and I think it is very helpful to
many of us. Can you please elaborate on that last part about binning?
What is that for? I must have missed something there.
Thanks,
Susan
Susan Willavize, Ph.D.
Global Pharmacometrics Group
860-732-6428
This e-mail is classified as Pfizer Confidential; it is confidential and
privileged.
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Nick Holford
Sent: Wednesday, July 23, 2008 6:32 AM
To: [email protected]
Subject: Re: FW: [NMusers] PPC
Paul,
The procedure you describe is a way of producing a posterior predictive
check but I don't know of any good examples of its use. A simpler way of
doing a PPC samples the population parameter estimates from a
distribution centered on the final estimates with a variance-covariance
based on the estimated standard errors and their correlation. VPCs are
not posterior predictive checks because they do not take account of the
posterior distribution of the parameter estimates (i.e. the final
estimates with their uncertainty). A VPC typically ignores the parameter
uncertainty and uses what has been called the degenerate posterior
distribution (See Yano Y, Beal SL, Sheiner LB. Evaluating
pharmacokinetic/pharmacodynamic models using the posterior predictive
check. J Pharmacokinet Pharmacodyn. 2001;28(2):171-92 for terminology,
methods and examples).
When I spoke of uncertainty I did not mean random variability (OMEGA and
SIGMA). A VPC will simulate observations using the final THETA, OMEGA
and SIGMA estimates.
You can calculate distribution statistics for your observations (such as
median and 90% intervals) by combining the observations (one per
individual) at each time point to create an empirical distribution. The
statistics are then determined from this empirical distribution. In
order to get sufficient numbers of points (at least 10 is desirable) you
may need to bin observations into time intervals e.g. 0-30 mins, 30-60
mins etc.
Nick
Paul Matthew Westwood wrote:
________________________________________
From: Paul Matthew Westwood
Sent: 22 July 2008 13:20
To: Nick Holford
Subject: RE: [NMusers] PPC
Nick,
Thanks for your reply and apologies once again for another confusing
email. I think I am using VPC, which as I understand it is simulating n
datasets using the final parameter estimates gained from the final
model, and then taking the median and 90% confidence interval (for
example) for each simulated concentration and comparing these to the
real concentrations. Whereas, PPC is where you then run the final model
through the simulated datasets and compare selected statistics of these
new runs with the original. Is this correct? You mentioned including
uncertainty on the parameter estimates in the simulated datasets. Would
one usually not include uncertainty (fixing the error terms to zero) in
the simulated datasets? Doing this with mine obviously produced much
better concentrations with no negative values and no 'significant'
outliers. Another thing you mentioned is comparing the median of the
simulated concentrations with the median of the original dataset
concentrations, but as there is only one sample for any particular time
point would this indicate the unsuitability of VPC (and furthermore PPC)
for this model?
Thanks again,
Paul.
________________________________________
From: [EMAIL PROTECTED] [EMAIL PROTECTED] On
Behalf Of Nick Holford [EMAIL PROTECTED]
Sent: 22 July 2008 10:30
To: [email protected]
Subject: Re: [NMusers] PPC
Paul,
Its not clear to me if you did a VPC (visual predictive check) using
just the final estimates of the parameters) or tried to do a posterior
predictive check (PPC) including uncertainty on the parameter
estimates
in the simulation.
I dont have any experience with PPC but I dont think its helpful for
model evaluation. Its more of a tool for understanding uncertainties
of
predictions for future studies.
I assume you dont have complications like informative dropout
processes
to complicate the simulation so if you did a VPC and the median of the
predictions doesnt match the median of the observations then your
model
needs more work.
Some negative concs are OK but 'impossibly high values' point to
problems with your model.
So I think you can safely say the VPC has worked very well -- it has
told you that you need to think more about your model. You might find
some ideas in these references:
1. Tod M, Jullien V, Pons G. Facilitation of drug evaluation in
children by population methods and modelling. Clin Pharmacokinet.
2008;47(4):231-43.
2. Anderson BJ, Holford NH. Mechanism-Based Concepts of Size and
Maturity in Pharmacokinetics. Annu Rev Pharmacol Toxicol.
2008;48:303-32.
Nick
Paul Matthew Westwood wrote:
Hello all,
I wonder if someone can give me some tips on PPC.
I am working on a midazolam dataset with a pediatric population, and
have decided to use PPC as a model validation technique. The dataset I
am modelling has up to 43 patients, at different ages, different
weights, different times of dosing and sampling, and different doses. I
simulated 100 datasets using NONMEM VI, fixing all parameters to the
final estimates from the model. The simulated datasets produced had a
large proportion of negative concentrations, and also a few impossibly
large concentration values. Also the median, 5th and 95th percentiles
were not very promising, and the resulting graphs not very clean.
Firstly, can I use PPC with any degree of confidence with a dataset
such as this, and if so, do I omit the negative concentration values
from the analysis?
Thanks in advance for any help given.
Paul Westwood,
PhD Student,
QUB,
Belfast.
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New
Zealand
[EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
