Douglas,
The answer is obvious:
The 'right' post hoc PK estimate of a PK parameter is from the PK data.
The 'right' post hoc PKPD estimate of a PK parameter is from the PKPD data.
The beauty of the Bayesian (aka post hoc) method is that you use all the
information you would like to believe in :-)
Nick
On 21/02/2013 9:48 p.m., Eleveld, DJ wrote:
Hi All,
The strange thing to me about the PPP&D method is that you generate two different
posthoc estimates for individual PK, one from the PK modelling alone and another from
the PPP&D step.
Does anyone know if one of these inferior to the other? Which is the "right"
individual posthoc PK estimate?
Warm regards,
Douglas Eleveld
-----Oorspronkelijk bericht-----
Van: [email protected] [mailto:[email protected]] Namens
Perez Ruixo, Juan Jose
Verzonden: February 21, 2013 7:25 AM
Aan: Ken Kowalski; 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers'
Onderwerp: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in
PK when doing PK-PD analysis
Hi All,
The discussion below is valid when PD is not affecting PK. If PD affects PK, as
in the case of some biologics, the sequential approaches may provide biased
estimates, and the simultaneous fit is probably the best option.
Regards,
Juan.
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Ken Kowalski
Sent: miércoles, 20 de febrero de 2013 9:1
To: 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers'
Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in
PK when doing PK-PD analysis
Hi Jeroen,
I believe the feature that you describe for a simultaneous fit also applies to the PPP&D
sequential approach that Nick advocates (which I also like). The framework of the PPP&D
approach is to set it up the same as you would a simultaneous model fit but you fix the PK
parameters (PK elements of theta, omega and sigma) to the final estimates from an independent
fit to the PK data alone. It is a sequential approach that does not use the posthoc estimates
of the PK parameters directly in the specification of the model as does the IPP sequential
approach. The PPP&D approach by its sequential nature does not account for the correlation
between the PK and PD parameter estimates. This can be a drawback or a feature of the approach
depending on the level of model misspecification. When there is substantial PD model
misspecification, a simultaneous model fit can lead to biased estimates of the PK parameters.
The PPP&D approach guards against PD model misspecification impacting the PK parameter
estimates since they are held fixed based on a separate (independent) fit to the PK data alone.
Best,
Ken
Kenneth G. Kowalski
President & CEO
A2PG - Ann Arbor Pharmacometrics Group, Inc.
110 Miller Ave., Garden Suite
Ann Arbor, MI 48104
Work: 734-274-8255
Cell: 248-207-5082
Fax: 734-913-0230
[email protected]
www.a2pg.com
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Elassaiss - Schaap, J (Jeroen)
Sent: Wednesday, February 20, 2013 8:38 AM
To: Nick Holford; nmusers
Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in
PK when doing PK-PD analysis
Hi,
Let me clarify/call out one aspect that may not be obvious to all, it is
underlying all the excellent replies (not to say the new abbreviations ;-).
Shrinkage is only a problem in sequential analysis but not in simultaneous
analysis. In sequential analysis one relies on posthoc estimates that are
impacted by shrinkage; the simultaneous approach used variance estimates
(omegas) which are not impacted.
And, as others pointed out, PK shrinkage may not be the first aspect one needs
to worry about in developing a PK-PD model but it is very useful to consider
its potential impact in the more final stages of model development.
Simultaneous analysis also has the benefit of accounting for correlation
between PK and PD parameters and therefore provides a better handle for
simulations in uncertainty.
Best regards,
Jeroen
J. Elassaiss-Schaap Senior Principal
Scientist Phone: + 31 412 66 9320
MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR
4406 | PO Box 20, 5340 BH Oss, NL
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Nick Holford
Sent: Tuesday, February 19, 2013 2:16
To: nmusers
Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in
PK when doing PK-PD analysis
Leonid,
Thanks for pointing out the confusion in my response. I intended to write:
"Sequential methods are preferred over
simultaneous methods whenever there is a possibility of mis-specification of the
model linking concentration to effect. This is nearly always a real risk so the
simultaneous method is rarely a sensible choice (see Zhang et al part II). "
The VIOA (very important old abbreviations
<http://www.cognigencorp.com/nonmem/nm/99dec192005.html>) IPP, PPP&D are
defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar
with these papers.
The simulations performed by Zhang et al. show that simultaneous can be worse than
sequential (see Part II paper). That is why I encourage a sequential approach using
the PPP&D method.
Best wishes,
Nick
On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote:
Hi Nick,
I am afraid, many users are not very familiar with all the important
abbreviations - PPP&D, IPP, VINA, etc. :)
I am not sure that I follow your points, looks contradictory:
Sequential methods are preferred ....
...sequential method is rarely a sensible choice
In any case, I would not agree that PPP&D (use of both PK and PD data
in the simultaneous fit ?) is the only good method (or even that this
method is the best in all situations). In many cases, sequential
method gives nearly identical results to the simultaneous fit, and was
easier to implement numerically. I would be curious to see a good real
life example where sequential method provided the wrong answer while
PP&D was correct (that is, the example where these two methods
resulted in different clinically relevant conclusions).
Leonid
VINA = Very Important New Abbreviation
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
On 2/18/2013 3:23 PM, Nick Holford wrote:
Jakob, Leonid,
I think it should be pointed out more clearly that PKPD analyses can
be performed most simply using the PPP&D method. This method has
better properties compared with IPP and is easier to set up and run
(see Zhang et al Part I). Sequential methods are preferred over
simultaneous methods whenever there is a possibility of
mis-specification of the model linking concentration to effect. This
is nearly always a real risk so the sequential method is rarely a
sensible choice (see Zhang et al part II).
The IPP+SE method has properties similar to PPP&D but it technically
more challenging to use and cannot be used without a
variance-covariance matrix of the estimates. This can usually only be
obtained with oversimplified models (asymptotic $COV) or by time
consuming bootstraps.
The bottom line is to use PPP&D.
Leonid makes a good point about using VPC for model evaluation. The
VPC is capable of detecting structural model misspecification for
PKPD analyses but it is not foolproof. A misspecified random effects
model can compensate for a misspecified structural model. See
references below for an example.
Best wishes,
Nick
Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive
Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434]
(last accessed 11 February 2012).
http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf
On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote:
Hi Matts,
One method to investigate the problem would be to conduct VPC. If
VPC with model-estimated variances provides good (not inflated)
range of PK profiles then one can argue that the PK model provides
good description of the data and can be used for simulations
(including PK-PD).
Another test could be to do VPCs for the PK-PD model: one with fixed
PK parameters (as was used in the sequential PK-PD modeling
procedure) and the other one with model-simulated ETAs for both PK
and PD parts. Again, if both provide good coverage of observed PK-PD
data then combination of PK and PD models can be trusted, and any of
the approaches can be applied. If one of the VPCs is inadequate,
than it should be noticeable in the too narrow or too wide
prediction intervals.
Leonid
On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote:
Resending, since my posting from this morning (below) has not yet
appeared on nmusers.
Apologies for any duplicate postings!
*From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal,
Matts"; [email protected] *Cc:* Ribbing, Jakob
*Subject:* RE: Simulation settgin in the precence of Shrinkage in PK
when doing PK-PD analysis
Hi Matts,
I think you are correct; the problem you describe has not had much
(public) discussion.
It is also correct like you say that this is mostly a problem in
case of all of the below
·sequential PK-PD analysis is applied (IPP approach, Zang et al)
·non-ignorable degree of shrinkage in PK parameters _of relevance_
oOf relevance: the PK parameters effectively driving PD for the
mechanism, e.g. CL/F if AUC is driving. In addition, if PD response
develops over several weeks/months then shrinkage in IOV may be
ignored even for relevant PK parameters
·I would also like to add that for this to be an issue individual PK
parameters must explain a fair degree of the variability in PD,
which is not always the case
oIf driving PD with typical PK parameters (along with dose and other
PD covariates) does not increase PD omegas, compared to IPP, then
either PK shrinkage is already massive, or else it is not an issue
for the IPP-PD model
If only the IPP approach is possible/practical a simplistic approach
to simulate PD data is as follows:
·sample (with replacement) the individual PK parameters along with
any potential covariates (maintaining correlation between IIP and
covariates, i.e. whole subject vectors for these entities, but
generally not for dose since generally should only have only random
association with IPP or PK/PD-covariates)
·then use the re-sampled datasets for simulating PD according to the
PD model (driven by IPP, covariates, dose, etc). The degree of
shrinkage is then the same for PD estimation and simulation.
This approach may for example allow to simulate realistic PD
response at multiple dosing, based on only single dose PD. When the
MD data becomes available then one may find that variabilities shift
between PK and PD due to different PK shrinkage, but I would argue
the simulated PD responses still were realistic. This approach is
useful for predictions into the same population (especially if
sufficient number of subjects available for re-sampling), but may
not allow extrapolation into other populations where PK is projected
to be different.
When possible the obvious solution is to apply one of the
alternative approaches to simultaneous PK-PD fit; after you have
arrived at a final-IPP model.
If a simultaneous fit is obtainable/practical this is the best
option, but notice that e.g. if you have rich PK data in healthy and
no PK data in patients (plus PD data in both populations): You can
estimate separate omegas for PD parameters in healthy vs.
patients, but it may be difficult to tell whether patients higher PD
variability is due to PK shrinkage, or due to the actual PD
variability being higher in this population (or both). PD
variability may be confounded by a number of other factors that are
actually variability in PK (fu, active metabolites and bio phase
distribution, just to mention a few where information may be absent
on the individual level). Depending on the purpose of the modelling
this often not an issue, however.
As you suggest there may be rare situations with IPP where a more
complicated approach is needed, with a) simulation and re-estimation
of PK model, to obtain Empirical-Bayes Estimates based on simulated
data, and then feed these into the subsequent PD model. I would see
this as a last resort. There are pitfalls in that if PD parameters
have been estimated under one degree of PK shrinkage, then applying
these estimates to a simulated example with different PK shrinkage
requires adjustment of PD variability.
I am not sure anyone has had to go down that route before and if not
I hope you do not have to either. Maybe others can advice on this?
Best regards
Jakob
Two methodological references:
Simultaneous vs. sequential analysis for population PK/PD data II:
robustness of methods.
Zhang L, Beal SL, Sheiner LB.
J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16.
Simultaneous vs. sequential analysis for population PK/PD data I:
best-case performance.
Zhang L, Beal SL, Sheiner LB.
J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404.
e
--
Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical
Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag
92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46
23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Notice: This e-mail message, together with any attachments, contains information
of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA
08889), and/or its affiliates Direct contact information for affiliates is
available at
http://www.merck.com/contact/contacts.html) that may be confidential,
proprietary copyrighted and/or legally privileged. It is intended solely for
the use of the individual or entity named on this message. If you are not the
intended recipient, and have received this message in error, please notify us
immediately by reply e-mail and then delete it from your system.
________________________________
De inhoud van dit bericht is vertrouwelijk en alleen bestemd voor de
geadresseerde(n). Anderen dan de geadresseerde(n) mogen geen gebruik maken van
dit bericht, het niet openbaar maken of op enige wijze verspreiden of
vermenigvuldigen. Het UMCG kan niet aansprakelijk gesteld worden voor een
incomplete aankomst of vertraging van dit verzonden bericht.
The contents of this message are confidential and only intended for the eyes of
the addressee(s). Others than the addressee(s) are not allowed to use this
message, to make it public or to distribute or multiply this message in any
way. The UMCG cannot be held responsible for incomplete reception or delay of
this transferred message.
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
