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RE: FO vs FOCE, sequential vs simultaneous

From: Mats Karlsson <mats.karlsson>
Date: Tue, 9 Dec 2008 20:44:06 +0100

Dear Marc,

 

On a small detail, "This is preferable to running a simultaneous model under
FO, where only the population typical values would be used to drive the
second stage endpoint (PD or metabolite) model" It is not entirely true that
only the population typical values would be used to derive the second stage
endpoint model. You can easily convince yourself about this by doing such an
analysis with and without the PK data (even when fixing the pop PK
parameters). The FO objective function does recognize information in all
data within an individual, even across variables when these share parameters
(as PK and PD data does). Therefore the advantage of using sequential will
not be as large (if advantageous at all) compared to sequential for FO.

 

Best regards,

Mats

 

Mats Karlsson, PhD

Professor of Pharmacometrics

Dept of Pharmaceutical Biosciences

Uppsala University

Box 591

751 24 Uppsala Sweden

phone: +46 18 4714105

fax: +46 18 471 4003

 

From: owner-nmusers
Behalf Of Gastonguay, Marc
Sent: Tuesday, December 09, 2008 7:56 PM
To: Gibiansky Leonid; Xiao, Alan; Hussein, Ziad; nmusers nmusers
Subject: Re: [NMusers] FO vs FOCE, sequential vs simultaneous

 

There's an additional, related point to consider with respect to estimation
method, in selecting a simultaneous vs sequential approach....

 

In the case where simultaneous modeling under conditional estimation is not
feasible (run-time, convergence, etc), it is preferable to use a sequential
approach. In the first step, model PK (or parent) using conditional
estimation or FO/POSTHOC, and run the second sequential step (e.g. PD or
metabolite) conditioned on the individual estimates obtained in the first
step. By doing so, the second step (PD or metabolite) model will be driven
by individual conditional random effect estimates obtained the first step.
This is preferable to running a simultaneous model under FO, where only the
population typical values would be used to drive the second stage endpoint
(PD or metabolite) model.

 

For more on this point, see:

Zhang L, Beal SL, Sheiner LB. J Pharmacokinet Pharmacodyn. 2003
Dec;30(6):387-404. Simultaneous vs. sequential analysis for population PK/PD
data I: best-case performance.

 

Regards,

Marc

 

Marc R. Gastonguay, Ph.D.

President & CEO, Metrum Research Group LLC [www.metrumrg.com]

Scientific Director, Metrum Institute [www.metruminstitute.org]

Direct: 860-670-0744 Main: 860-735-7043

Email: marcg

 

 

 

 

On Dec 9, 2008, at 12:33 PM, Leonid Gibiansky wrote:





Hi Alan,

Here:

http://quantpharm.com/pdf_files/PAGE_2008_Poster_1268_web.pdf

I used all datasets that I had, and I was not able to find any problem where
FO was superior to FOCE.

Not-converged FOCE is better, in my opinion, than converged FO (although you
can always check using diagnostic plots).

If you cannot use FOCE due to time restrictions, it is better to use FO than
just abandon modeling. Still, I would try to run the final model with FOCEI.

Concerning sequential vs simultaneous: there are several points to consider,
and this is usually relates to the PK-PD case. For PK-PD, the main question
is the comparison of PK and PD variabilities. Usually, PK variability is
smaller, and PK data are more reliable. Then, sequential modeling can be
more warranted. If PK and PD variabilities are similar (both residual and
inter-subject) you can use joint fit. I usually do PK first, then PK-PD, and
then try to fit combined model at the very last stage.

For parent-metabolite case, both sets of data are equally reliable (or not
reliable), and variability is usually similar. Then the question boils down
to time and convenience. Again, I usually do parent fist, then fix
parameters and do metabolite, and then, if possible, do simultaneous fit.
This often saves time: parent model is more simple, it can be done in
standard ANDANs for 1-2 compartment models that are much quicker. You can
experiment freely with random effect, covariates, residual error, etc. Joint
model often needs to be solved using ADVAN5, 7 or even $DES which are more
CPU-consuming. You want to do minimum number of runs here. Thus, you want to
start with good parent model, and study metabolite part only. The final
joint run fits all parts together.

Thanks
Leonid




--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566




Xiao, Alan wrote:



Dear All,

I know this is an old topic, too, but would like to see the statistics. When
you have a dataset with about 10% of dense Phase II data (predose, 2, 4, 8,
and 12 hrs post dose on day 1 and at steady state, twice-daily dose regimen)
and about 90% of very sparse Phase III data (1-2 samples/patient), which
method do you prefer: FO or FOCE? or FO for model development but FOCE for
model refinement/finalization? If FOCE is not practical because of long
run-time or numerical difficulties in converge, do you stop here or would
you use FO?

Thanks,

Alan

 

 


Received on Tue Dec 09 2008 - 14:44:06 EST

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