RE: PK analysis of data from different occasions
Hej Havard,
I agree with David that you are much better off with a model that
includes all your data. I think the idea of modeling is to be able to
explain the whole system and not just a part of it.
It seems that you have some changes from one occasion to another. The
logical procedure would be to come up with a hypothesis to explain the
reasons for the observed change. Adding variability terms might improve
the fit but the real question is why there are such big variations
between the occasions that your structural model changes from one day to
another. I am not dismissing the idea of estimating IIV or IOVs (inter
occasional variability). This might very well be real and due to e.g.
intake of food on one day and no food on another day. However, if you
think your compound changes its own duration of absorption (e.g. through
a change in gastric motility), changes its own extent of absorption
(e.g. inducing or inhibiting gut wall or liver enzymes), or affects its
own elimination, you should add it in your structural model. There are
several publications readily available that deal with these types of
changes. The problem you might face could insufficient data due to e.g.
non-optimal study design.
Ha det!
Toufigh
Quoted reply history
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Havard Thogersen
Sent: Tuesday, July 31, 2007 7:48 AM
To: [email protected]
Subject: [NMusers] PK analysis of data from different occasions
Dear nmusers,
I'm working on an PK analysis of a data set in NONMEM and have run into
some questions. I would really appreciate if any of you have the chance
to give me some suggestions.
The data set I'm analyzing consist of concentration-time profiles
obtained at three different occasions for every subject (n=20).
Non-compartmental analysis and individual modeling indicate changes in
CL/F between the occasions, possibly explained by renal function. If I
analyze the occasions separately, a two-compartment model describes the
data well at occasions 2 and 3, but not at occasion 1. At occasion 1
large inter-individual variation is observed and a one-compartment model
can to some degree describe the data. If I analyze all occasions
simultaneously with between-occasion variation, a one-compartment model
is able to fit the data reasonably well (two-compartment model is not
considered robust). The parameter estimates are in the right ball park,
but GOF plots from the simultaneous analysis indicate model
misspecification typical for a one-compartment model fit of
"two-compartment data".
My question is therefore how I should proceed? Will separate or
simultaneous modeling be most ideal and what should I consider to get
most relevant information?
Best regards,
Havard
Havard Thogersen,
Master student
University of Oslo/Cincinnati Children's Hospital Medical Center
Pediatric Pharmacology Research Unit
and Laboratory of Applied Pharmacokinetics & TDM
Cincinnati Children's Hospital Medical Center
3333 Burnet Avenue, MLC 6018
Cincinnati, OH 45229-3039
Phone: (513) 636-9011