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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UNIVERSITY OF CAPE TOWN
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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
This e-mail is subject to the UCT ICT policies and e-mail disclaimer published
on our website at http://www.uct.ac.za/about/policies/emaildisclaimer/ or
obtainable from +27 21 650 9111. This e-mail is intended only for the person(s)
to whom it is addressed. If the e-mail has reached you in error, please notify
the author. If you are not the intended recipient of the e-mail you may not
use, disclose, copy, redirect or print the content. If this e-mail is not
related to the business of UCT it is sent by the sender in the sender's
individual capacity.
###
