Hi Paolo,
It is a bit late to chime in but I can't resist... Great discussion point! I am
of the opinion that if we develop models robustly, it in the end should not
matter. If we would introduce a structural bias by neglecting BOV early on, we
should be able to see a reflection of that in a diagnostic plot after
introduction of BOV. And that in turn should lead to evaluation of other
structural models. But this obviously depends on close scrutiny of diagnostics
and frequent back-tracing.
Perhaps the question could be restated as: which method is more efficient? -
retaining the original answer.
It may even be generalized by stating that those model parts that describe most
of variance in the most plausible manner should be introduced first. This
should prevent bias that complicates evaluation of more detailed parts because
of nonlinearity issues as you described.
Such a rule could be applied to any model and result in e.g. BSV on baseline be
added early on for a PK-PD problem, body weight for general PK, BOV for
multi-occasion/rich sampling problems, to name a few.
Last but not least, I skip breakfast completely ;-).
Best regards,
Jeroen
Modeling& Simulation Expert
Pharmacokinetics, Pharmacodynamics& Pharmacometrics (P3) - DMPK
MSD
PO Box 20 - AP1112
5340 BH Oss
The Netherlands
[email protected]
T: +31 (0)412 66 9320
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F: +31 (0)412 66 2506
www.msd.com
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Paolo Denti
Sent: Wednesday, 17 November, 2010 17:23
To: Elodie Plan
Cc: 'nmusers'
Subject: Re: [NMusers] Zähneputzen VOR oder NACH dem Frühstück? What comes
first? BSV, BOV, or covariates?
Thank you Elodie,
the reference you mention also states that the covariates were tested only on parameters
for which BOV and BSV were significant. This is generally the approach I use, so that I
can test whether the mentioned variabilities are indeed explained with the inclusion of
covariates. I wonder if somebody can think of any exceptions to this "rule"?
Also, both Oscar della Pasqua and Coen Van Hasselt pointed to me this PAGE
poster (unfortunately presented in a literally burning hot poster session in
Berlin):
http://www.page-meeting.org/default.asp?abstract=1887
which seems to stress that disregarding BOV might lead to model
misspecification.
I also got a reply from Alwin Huitema, who told me that his experience with
modelling in HIV is that ignoring IOV early in the modelling process might
guide to wrong models.
Any supporters of an alternative approach or shall I just assume that I was
doing the same as everybody else?
Who would brush teeth before breakfast anyway? ;) Another, safer, option is
suggested by Oscar:
Paolo,
By the way, hygiene rules do suggest you brush your teeth before and after
breakfast.
I don't want to infer that this is the same for modelling but I can
say that you can recognise the individual ingredients in your
breakfast if your taste butts are clean:)
Oscar
Ciao,
Paolo
On 16/11/2010 22:15, Elodie Plan wrote:
Dear Paolo,
Thanks for this interesting NMusers thread.
I think the order you are describing really makes sense in theory, for
the reasons you describe, but in brief because it seems covariates
should be incorporated on a model already fully developed structurally
and statistically, so this includes IOV. Moreover, the covariates will
increase the predictive performance (and the understanding) of the
model, by being introduced on structural parameters, but also possibly
directly on IIV and IOV.
I also wanted to verify that this was what was done in practice, there
were
6 entries when searching for "occasion AND covariate AND NONMEM" on
PubMed, I can recommend the following where the decrease in
variability magnitude following the covariate model building is nicely
discussed: Sandström M, Lindman H, Nygren P, Johansson M, Bergh J,
Karlsson MO. Population analysis of the pharmacokinetics and the
haematological toxicity of the fluorouracil-epirubicin-cyclophosphamide regimen
in breast cancer patients.
Cancer Chemother Pharmacol. 2006 Aug;58(2):143-56.
Best regards,
Elodie
PS: IOV or breakfast, I like it first :)
Elodie L. Plan, PharmD, MSc, PhD student
********************************************
Uppsala Pharmacometrics Research Group Department of Pharmaceutical
Biosciences P.O. Box 591, SE-751 24 Uppsala, SWEDEN Mob +46 76-242
1256, Skype "ppeloo"
-----Original Message-----
From:[email protected]
[mailto:[email protected]] On Behalf Of Paolo Denti
Sent: Tuesday, November 16, 2010 10:10 AM
To: nmusers
Subject: [NMusers] Zähneputzen VOR oder NACH dem Frühstück? What comes
first? BSV, BOV, or covariates?
Dear all,
don't be discouraged by the subject, this is indeed NMUsers and not
German 101, and this post is about pharmacometrics, please read on...
;)
The subject of the message comes from when I was studying German, and
from an exercise in our book with lots of colourful pictures. The
point of the exercise was only to teach us how to say "tooth
brushing", "have breakfast", "before" and "after", but instead it
sprouted a lively discussion in the class about what comes first and
last in everybody's morning routine... So I thought it would be an
appropriate title for this post, which is a survey/question about what
modelling approach people use/recommend for model development.
Just to contextualize a bit, here at UCT we mainly study HIV and TB
drugs, which are dosed repeatedly (once or twice per day) and administered
orally.
We often have data available on more than one sampling occasion, and
many times these occasions are virtually
equivalent: no changes in co-treatment or other covariates, just a
mere repetition of the experiment on a different day. Confirming what
Mats recently pointed out in a post about the use of BOV, our
experience is that, especially in the absorption phase, the
contribution of BOV is dominant, and cannot be ignored. The absorption
is often subject to random delays and factors that are mostly
occasion-specific and not measurable/available in the dataset.
Therefore, when I start modelling new data, I normally proceed as follows:
1. I initially assume every occasion as a separate profile, either
using dummy IDs (and pretending it's different subjects) or coding all
variability as BOV. I believe this allows the maximum flexibility to
test the structural model, and I find that, if I don't proceed like
this, I may run into troubles detecting the correct structural model.
In this early stage of model development, I mostly use individual
plots, and try to see if my prediction profile is flexible enough to run
through the points.
2. Then I try to see if some of the variability is subject-specific
(normally V and CL) and can be better explained either by only BSV or
both BSV and BOV. I use the OFV to guide this process, but if the BOV
is much larger than BSV, and physiology supports the hypothesis that
the parameter be occasion-specific, I tend to disregard BSV.
3. Once I believe I got my structural model right, and organized the
hierarchy of random variability in a decent way, I start incorporating
the covariates. If they turn out to be significant, I see that BOV and
BSV decrease, and sometimes become superfluous in the model and can be removed.
I know other modellers would recommend first introducing BSV and/or
covariates, before considering BOV and I would be interested in
knowing people's opinion about this. Each method probably has its pros
and cons, and I would really value your input about this topic. What
are the advantages and disadvantages of the different approaches?
Since I favour the modus operandi I just explained, I give my reasons,
and look forward to some comments. My opinion (but I am obviously
biased) is that it does not hurt to include BOV first, since it is
easy to remove from the model if the same variability is explained by
covariates, and likely, if this is the case, BOV will decrease in size.
On the other hand, disregarding BOV might prevent the identification
of the correct structural model. I am thinking, for example, about a
comparison between 2-cmpt vs 1-cmpt when the absorption is subject to
substantial random delays. If BOV is not considered, this is
equivalent to pooling the data from all occasions, with the potential
result of having a cloud of points without much structure... And also,
as a general rule, I would allow a parameter to move with an ETA,
before I try to explain its changes with a covariate effect. In this
way I can also test better if the covariate is explaining some of this
variability.
Ok, I've been once again way too lengthy, apologies. Any comments/thoughts?
In other words, do you first brush your teeth or have breakfast?
Please join the survey! ;)
Greetings from Cape Town,
Paolo
PS Ich putze die Zähne immer NACH dem Frühstück... I can't enjoy
coffee with that minty toothpaste after-taste... :)
--
------------------------------------------------
Paolo Denti, PhD
Post-Doctoral Fellow
Division of Clinical Pharmacology
Department of Medicine
University of Cape Town
K45 Old Main Building
Groote Schuur Hospital
Observatory, Cape Town
7925 South Africa
phone: +27 21 404 7719
fax: +27 21 448 1989
email:[email protected]
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------------------------------------------------
Paolo Denti, PhD
Post-Doctoral Fellow
Division of Clinical Pharmacology
Department of Medicine
University of Cape Town
K45 Old Main Building
Groote Schuur Hospital
Observatory, Cape Town
7925 South Africa
phone: +27 21 404 7719
fax: +27 21 448 1989
email:[email protected]
------------------------------------------------
###
UNIVERSITY OF CAPE TOWN
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