RE: Rational of using IOV
Nick
While I agree that BOV is not solely a nuisance parameter it is a design
specific parameter and hence can be somewhat of a nuisance. By design specific
we can formulate settings in which the design of the study changes the estimate
of BOV.
To estimate the variance between occasions the duration of the occasion needs
to be defined (a priori). If the occasion is long then the estimate of BOV
will tend to zero since the integral over the occasion to get the average
parameter value will integrate over the random variability. If the occasion is
short then it will tend to a larger positive number. Imagine an occasion of 1
hour versus 1 year. I realise that most tend to use a dose interval as an
occasion but this is also arbitrary as is clinic visits. The duration of the
occasion would need to be indexed to the substantive inferences of the model to
ensure that any influence that BOV has can be assessed in terms of model
predictions.
Given that BOV is design specific then how should this be interpreted in any
given circumstance? Note that being design specific doesn't preclude the
benefit of BOV in its role as an estimable but nuisance parameter (i.e. to
reduce bias in estimates of the population mean parameter values).
Steve
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Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Nick Holford
Sent: Tuesday, 2 November 2010 8:33 a.m.
To: [email protected]
Subject: Re: [NMusers] Rational of using IOV
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
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