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Hi Lauren:
I don't know of workshops in WinNonLin either. But if you are
interested in real population modeling software leading to optimal
dosage regimens, which also has the guarantee that the more patients
you study the closed your results get to the truth (statistical
consistency), and which are among the most precise known (statistical
efficiency), think of the nonparametric approach with the USCPACK
software. Population modeling may have begun with NONMEM, but it
certainly has not ended there. I don't know of any currently widely
used software that beats this approach. Look at our web site and see
New advances in population modeling, and "teaching topics", and a
comparison of methods that use exact calculations of the likelihood
versus those that use approximations such as FO or FOCE. For example,
we recently presented at the3 IATDM-CT meetings in Louisville the
following poster, which is attached. Further, in a comparison of
population modeling software based on a careful Monte Carlo simulation
study,
Dr. Leary simulated a two-parameter truly Gaussian population
ranging from 25 to 800 subjects, and compared the results found with 1)
IT2B (using the FOCE approximate parametric likelihood), 2) PEM (with
an accurate parametric likelihood [42]), and 3) with NPAG and NPOD
(with exact nonparametric likelihoods). The model had parameters Vol
and Kel. Over 1000 replications were done. Populations ranged from 25
to 800 subjects. A single bolus intravenous dose, and 2 simulated serum
concentrations, each with a 10% standard deviation, were used. Results
with both NPAG and PEM were consistent, with estimates more closely
approaching the true values as the number of subjects increased. The
FOCE IT2B did not have such consistent behavior. A small bias in mean
values of 1 - 2 % was seen with FOCE. As to variances, NPAG and PEM
were again consistent, but the bias of FOCE was quite significant,
about 20 - 30%. As to correlation coefficients, consistent behavior was
again seen with NPAG and PEM. Severe bias was seen with FOCE.
Even more disturbing was the loss of statistical efficiency with the
FOCE approximation. Recently [45], this work was extended, with Dr.
Ruedi Port of the German Cancer Research Institute, to include the FO
and FOCE approximations as implemented in the parametric population
modeling program NONMEM. NONMEM FO had biases as high as 50% in
estimates of variances, and statistical efficiencies less than 2% of
those of the accurate likelihood PEM and NPAG methods for 800 subjects.
NONMEM FOCE was a modest improvement over its IT2B FOCE counterpart.
However, NONMEM FOCE still had significantly compromised statistical
efficiency, less than half that of the accurate likelihood methods, as
shown below:
Estimator Relative efficiency Relative
error
DIRECT OBSERVATION 100.0 % 1.00
PEM 75.4% 1.33
NPOD 61.4% 1.63
NONMEM FOCE 29.0% 3.45
IT2B FOCE 25.3% 3.95
NONMEM FO 0.9% 111.11
A Recent Competition. Recently an international blind trial of seven
parametric population PK/PD estimation methods was conducted under by
INSERM in Lyon, France. One hundred simulated data sets from a
sigmoidal PD dose/response model were sent in May, 2004 to a variety of
PK/PD software vendors and academic developers. Both standard (e.g.,
NONMEM and NLME) nonlinear mixed effects methods based on FOCE
likelihood approximations and new approaches (simulated likelihood,
stochastic approximation, and parametric EM methods, including our PEM)
were included. In September, 2004, participants met in Lyon and the
results were revealed. In general, methods based on more precise
likelihood evaluation techniques significantly outperformed those using
FOCE approximations. Our PEM tied for the overall best performance
among all seven methods as measured by criteria such as RMSE of the
estimated parameter values relative to the true values, and the bias of
the model predictions. In particular, PEM had the best overall
performance in correctly identifying which data sets had a significant
gender covariate dependence and which did not. In addition, when it is
not known whether the distributions are Gaussian or not, NPOD or NPAG
are very efficient and useful methods for population PK/PD modeling, as
also shown above.
Very best regards,
Roger Jelliffe
Roger W. Jelliffe, M.D. Professor of Medicine,
Division of Geriatric Medicine,
Laboratory of Applied Pharmacokinetics,
USC Keck School of Medicine
2250 Alcazar St, Los Angeles CA 90033, USA
Phone (323)442-1300, fax (323)442-1302, email= jelliffe.-a-.usc.edu
Our web site= http://www.lapk.org
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The following message was posted to: PharmPK
Dear Roger,
I would like to make a few comments to your reply to Lauren. Putting aside
the issue what you mean with 'real population modeling software', I agree
with your statement that population modeling has not ended with NONMEM.
You described an example of a simulation study by Dr. Leary. About one
year
ago I had some correspondence with you and Dr. Leary about this study and
the results. I had repeated this study with the program MW\Pharm (module
KinPop, written by myself; available from Mediware BV, The Netherlands
(www.mediware.nl)) using an Iterative Two-Stage Bayesian (ITSB) approach
for
population analysis, based on the method described by Mentre and Gomeni (J
Pharm Stat 1995; 5: 141-158) and by Bennett and Wakefield (J Pharmacokinet
Biopharm 1996; 24: 403-432).
The performance of the ITSB algorithm was quite good, and comparable to
that
of PEM and NPAG, the programs cited in your message. For example, the
efficiency was about the same as that of the value of PEM. The only
deviation was a bias of about 1% in k, even for high numbers of patients.
However, given the fact that this value is still smaller than the RMSE
using
800 patients, and clinically insignificant, this limitation is of academic
interest only.
Interestingly, the ITSB algorithm as implemented in MW\Pharm performed
much
better than the USC*PACK IT2B program, although both programs are based on
the same Bayesian approach. The reason for the apparent discrepancy
between
MW\Pharm and IT2B is unclear. Another interesting finding was the relative
good performance of the Standard Two-Stage (STS) procedure; in this case
of
two measurements and two parameters, STS does not require any fitting
procedure, and can be performed in a simple spreadsheet. STS performed
less
well than PEM, NPAG and MW\Pharm (although parameter estimates were still
rather good), and markedly better than IT2B. Also, the efficiency of STS
was
close to that of these programs. This would imply that STS performs also
better than NONMEM. This finding throws a new light on the question with
respect to the begin (STS, not NONMEM) and end of population modeling.
Best regards,
Hans Proost
Johannes H. Proost
Dept. of Pharmacokinetics and Drug Delivery
University Centre for Pharmacy
Antonius Deusinglaan 1
9713 AV Groningen, The Netherlands
tel. 31-50 363 3292
fax 31-50 363 3247
Email: j.h.proost.aaa.rug.nl
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Copyright 1995-2010 David W. A. Bourne (david@boomer.org)