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RE: FO vs FOCE, sequential vs simultaneous

From: Ken Kowalski <ken.kowalski>
Date: Tue, 9 Dec 2008 14:27:12 -0500


The method that Marc describes is labeled the PPP&D method in the Zhang et
al paper below. With this approach you set up the model just as if you were
going to do a simultaneous fit (that is the dataset contains DVs for both
the PK and PD (or metabolite)) but all of the population PK parameters
(thetas, omegas and sigmas) are fixed at the estimates from a separate model
fit to the PK (or parent) data alone (i.e., the first sequential step). As
Marc suggests, if you use FOCE in the second sequential step the model will
be driven by the individual conditional random effects obtained from the
first step since the PK data is included along with the PD data (or
metabolite) in the data file. I have had a lot of success using this
approach and it can certainly cut down on run-time as compared to the
simultaneous model fit.

 

Regards,

 

Ken

 

From: owner-nmusers
Behalf Of Gastonguay, Marc
Sent: Tuesday, December 09, 2008 1:56 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 - 14:27:12 EST

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