Dear all:
I'm now having a set of TDM data, only troughs C0 available.I intend to evaluated the appropriateness of the constructed model.My question is whether to use pcVPC or NPDE as a diagnostic tool in such a case?Which one is better? Or to use them both, as suggested by Bergstrand et al.: "The best practice most likely lies in using a wide toolbox of diagnostics, rather than one single universal test to decide whether a model is fit for purpose or not."
Thank you in advance.
Yours,
Chenyan Zhao
Email: zhaochenyanvictory_at_hotmail.com
Mobile: +86 13917430219
pcVPC or NPDE
Dear Chenyan,
Appropriateness is largely a matter of what the ultimate purpose of the
model is, and neither metric will be 'better' in all cases. Extrapolating
into a new population may require different evaluation diagnostics than
using a model to optimize the dose the observed population.
Given you only have trough samples, using a posterior predictive check on
trough levels or equivalence criteria such as proposed in:
1.
Jadhav, P. R. & Gobburu, J. V. S. A new equivalence based metric for
predictive check to qualify mixed-effects models. *AAPS J* *7,* E523–E531
(2005).
would likely work well.
Devin Pastoor
Clinical Research Scientist, PhD student
Center for Translational Medicine
University of Maryland, School of Pharmacy
Quoted reply history
On Fri, Sep 11, 2015 at 10:38 AM ZhaoChenyan <[email protected]>
wrote:
> Dear all:
>
> I'm now having a set of TDM data, only troughs （C0 ） available.
> I intend to evaluated the appropriateness of the constructed model.
> My question is whether to use pcVPC or NPDE as a diagnostic tool in such a
> case?
> Which one is better?
> Or to use them both, as suggested by Bergstrand et al.: "The best
> practice most likely lies in using a wide toolbox of diagnostics, rather
> than one single universal test to decide whether a model is fit for purpose
> or not."
>
>
>
> Thank you in advance.
>
>
>
> Yours,
>
> Chenyan Zhao
>
> Email: *zhaochenyanvictory@**hotmail.com http://hotmail.com*
>
> Mobile: +86 13917430219
>
>
>
Dear Chenyan and Devin,
Chenyan: Nice of you quote me ;) I still stand by the old quote. In my opinion
the advantage with the pcVPC is that it is easy to diagnose if the model
accurately predicts both the central trend as well as the variability of the
data. The NPDEs on the other hand usually makes for a faster diagnostic that do
not require any binning of the data for a rough interpretation. For you own
interpretation I recommend you use both types of diagnostics (possibly with
different x-axis variables, time, dose etc.) and for a publication you should
use whatever you think will be easiest for the audience to interpret.
Devin: The posterior predictive check (PPC) you suggest will only be
appropriate if you have a model that also predicts the dose alteration
decisions (TDM). With TDM dosing a PPC with the actual dosing history will
result in an over prediction of the variability just like VPCs has been
demonstrated to do.
Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual
predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011
Jun;13(2):143-51. doi: 10.1208/s12248-011-9255-z.
Best regards,
Martin
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Devin Pastoor
Sent: den 11 september 2015 16:46
To: ZhaoChenyan; [email protected]
Cc: [email protected]
Subject: Re: [NMusers] pcVPC or NPDE
Dear Chenyan,
Appropriateness is largely a matter of what the ultimate purpose of the model
is, and neither metric will be 'better' in all cases. Extrapolating into a new
population may require different evaluation diagnostics than using a model to
optimize the dose the observed population.
Given you only have trough samples, using a posterior predictive check on
trough levels or equivalence criteria such as proposed in:
1.
Jadhav, P. R. & Gobburu, J. V. S. A new equivalence based metric for predictive
check to qualify mixed-effects models. AAPS J 7, E523–E531 (2005).
would likely work well.
Devin Pastoor
Clinical Research Scientist, PhD student
Center for Translational Medicine
University of Maryland, School of Pharmacy
On Fri, Sep 11, 2015 at 10:38 AM ZhaoChenyan
<[email protected]<mailto:[email protected]>> wrote:
Dear all:
I'm now having a set of TDM data, only troughs （C0 ） available.
I intend to evaluated the appropriateness of the constructed model.
My question is whether to use pcVPC or NPDE as a diagnostic tool in such a case?
Which one is better?
Or to use them both, as suggested by Bergstrand et al.: "The best practice most
likely lies in using a wide toolbox of diagnostics, rather than one single
universal test to decide whether a model is fit for purpose or not."
Thank you in advance.
Yours,
Chenyan Zhao
Email: [email http://hotmail.com
Mobile: +86 13917430219
Dear Martin, Chenyan, and Devin,
I agree that in a large number of cases it would probably not matter much which
of the two (pcVPC or NPDE) you use, however there are particular cases where I
think NPDE may have advantages over pcVPC. This is mainly when you are
evaluating clinical data with highly variable dosing (for instance due to
rescue medication or dose adjustments based on individual needs) or in
situations with large covariate effects.
This is because pcVPC averages simulated DV values for all individual profiles
combined over a certain bin and compares the observed DV values of all
individuals to this, but when the variability in the bin is large due to
inter-individual variability in dosing regimen or the influence of covariates,
this may not be very relevant (If there is only one major covariate effect,
stratifying the pcVPC may be an option, but with a large number of covariates
or a large range in continuous covariate values this may become impractical as
well). These issues can be overcome by NPDE, as the observed DV values of an
individual are compared to the simulated range of DV values for each separate
individual, taking individual dosing and covariate effects into account in the
simulations.
Kind regards,
Elke Krekels
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Martin Bergstrand
Sent: Monday, September 14, 2015 3:42 PM
To: Devin Pastoor; ZhaoChenyan; [email protected]
Cc: [email protected]
Subject: RE: [NMusers] pcVPC or NPDE
Dear Chenyan and Devin,
Chenyan: Nice of you quote me ;) I still stand by the old quote. In my opinion
the advantage with the pcVPC is that it is easy to diagnose if the model
accurately predicts both the central trend as well as the variability of the
data. The NPDEs on the other hand usually makes for a faster diagnostic that do
not require any binning of the data for a rough interpretation. For you own
interpretation I recommend you use both types of diagnostics (possibly with
different x-axis variables, time, dose etc.) and for a publication you should
use whatever you think will be easiest for the audience to interpret.
Devin: The posterior predictive check (PPC) you suggest will only be
appropriate if you have a model that also predicts the dose alteration
decisions (TDM). With TDM dosing a PPC with the actual dosing history will
result in an over prediction of the variability just like VPCs has been
demonstrated to do.
Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual
predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011
Jun;13(2):143-51. doi: 10.1208/s12248-011-9255-z.
Best regards,
Martin
From: [email protected]<mailto:[email protected]>
[mailto:[email protected]] On Behalf Of Devin Pastoor
Sent: den 11 september 2015 16:46
To: ZhaoChenyan; [email protected]<mailto:[email protected]>
Cc: [email protected]<mailto:[email protected]>
Subject: Re: [NMusers] pcVPC or NPDE
Dear Chenyan,
Appropriateness is largely a matter of what the ultimate purpose of the model
is, and neither metric will be 'better' in all cases. Extrapolating into a new
population may require different evaluation diagnostics than using a model to
optimize the dose the observed population.
Given you only have trough samples, using a posterior predictive check on
trough levels or equivalence criteria such as proposed in:
1.
Jadhav, P. R. & Gobburu, J. V. S. A new equivalence based metric for predictive
check to qualify mixed-effects models. AAPS J 7, E523–E531 (2005).
would likely work well.
Devin Pastoor
Clinical Research Scientist, PhD student
Center for Translational Medicine
University of Maryland, School of Pharmacy
On Fri, Sep 11, 2015 at 10:38 AM ZhaoChenyan
<[email protected]<mailto:[email protected]>> wrote:
Dear all:
I'm now having a set of TDM data, only troughs （C0 ） available.
I intend to evaluated the appropriateness of the constructed model.
My question is whether to use pcVPC or NPDE as a diagnostic tool in such a case?
Which one is better?
Or to use them both, as suggested by Bergstrand et al.: "The best practice most
likely lies in using a wide toolbox of diagnostics, rather than one single
universal test to decide whether a model is fit for purpose or not."
Thank you in advance.
Yours,
Chenyan Zhao
Email: [email http://hotmail.com
Mobile: +86 13917430219