Thierry,
Between subject variability (BSV aka IIV) and within subject variability
(WSV aka IOV) describe the limits of what we can identify as sources of
variability.
I don't consider this a nuisance -- it is an opportunity for learning.
The random assumption used for estimation of WSV is a convenient way of
describing the size of the problem. If we recognize there is a large
element of WSV then it may stimulate thinking and further investigation
to try and understand it.
Ignoring WSV will give a false impression about what can be gained from
TCI (aka TDM). TCI can only hope to remove the BSV part of unpredictable
variability.
See Holford NH. Target concentration intervention: beyond Y2K. Br J Clin
Pharmacol. 1999;48(1):9-13.
Best wishes,
Nick
On 2/11/2010 2:34 a.m., Buclin Thierry wrote:
Dear James,
I always thought that intra-individual variability (IIV) classically
represented the immovable limit on the gains to be expected from TDM
-- IOV being indeed used only in a minority of population PK analyses.
Both intra- and inter-occasion variability actually represent
nuisance. We agree on the point that specifying an IOV term in a model
will decrease the residual IIV. But wouldn't this precisely give a
falsely favorable impression about potential gains from a TDM program?
Am I wrong to think so?
Kind regards
Thierry
*De :*James G Wright [mailto:[email protected]]
*Envoyé :* lundi, 1 novembre 2010 14:04
*À :* Buclin Thierry
*Objet :* Re: [NMusers] Rational of using IOV
Dear Thierry,
I hope you are well. I think you are right to highlight the
importance of IOV for TDM, but I would argue it is very important to
include it in the model. This is because IOV places an immovable
limit on the gains from TDM. The classic error is to develop a TDM
strategy mistakenly lumping IOV with IIV, and drastically
over-estimating the utility of TDM.
Best regards, James
On 01/11/2010 11:55, Buclin Thierry wrote:
Hi Nicolas
My short answer would be another question: "what is the aim of your
analysis ?"
IOV is fine to split variability into inter-individual,
intra-individual-inter-occasion and intra-individual-intra-occasion
random components. This is excellent for data description, and can
bring interesting insight into the mechanisms explaining variability.
But if you want to use your results for prediction, e.g. to devise a
TDM program, you won't be able to draw relevant information from IOV:
a blood sample obtained in a patient on a certain occasion won't
inform you on your patient's behavior on another occasion; in this
situation, a model merely distinguishing inter-individual and
intra-individual variability components is easier to exploit. Thus,
there may be good reasons not to use IOV even when it would be possible.
Kind regards
Thierry
Thierry Buclin, MD, PD,
Division of Clinical Pharmacology and Toxicology
University Hospital of Lausanne (CHUV)
Lausanne - SWITZERLAND
tel +41 21 314 42 61
fax +41 21 314 42 66
On 1/11/2010 10:53 a.m., Nicolas SIMON wrote:
Dear colleagues,
could someone give me an advice about the rational of using IOV in a
particular circumstance?
We have data from a clin trial with 3 occasions for each patient. It
was a chemotherapy and the patients have received up to 7 cures. The
issue is that the 3 occasions differ from one patient to another.
Patient X may have be seen on cure 3, 5 and 7 while patient X+1 was
seen on cure 2, 5 and 6 or whatever...
It seems to me that combining the 1st occ of all patients is
meaningless (as for 2nd and 3rd).
Shall we use as many occasions as cures (7 in our dataset)? In that
case, how many patients by occ is relevant as a minimum? For certain
occ we may have few patients. Combining cures is hazardous and has no
clinical justification.
Best regards
Nicolas
Pr Nicolas SIMON
Universite de la Mediterranee (Aix-Marseille II)
--
James G Wright PhD,
Scientist, Wright Dose Ltd
Tel: UK (0)772 5636914
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology
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
