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
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: [EMAIL PROTECTED]
Quoted reply history
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
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
Quoted reply history
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
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: [EMAIL PROTECTED]
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
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
Quoted reply history
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
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: [EMAIL PROTECTED]
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
Leonid,
Thanks very much for this experimental data support confirming again the wisdom of using FOCE rather than FO and not worrying about convergence.
Nick
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
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford