If only there was an easy way to include all those occasions in the
model file...
https://www.popypkpd.com/#post-26 <https://www.popypkpd.com/#post-26>
Kind regards, James
On 12/08/2020 17:13, Nick Holford wrote:
Hi Tingjie,
I agree with Leonid's first remark. The natural way to account for the random
effect associated with each dosing occasion is to use the dosing occasion as
the covariate and implement the covariate effect using between occasion
variability. You only need to have enough occasions to cover the number of
doses which have an observation in the subsequent interval for the subject who
had the most such occasions. I have used BOV for twice daily dosing over
several months but only needed 40 occasions to describe all the pa-tients in a
large study.
You should also be thinking of BOV on bioavailability as well as lag time.
Also consider testing whether BSV for these absorption parameters is greater
than zero once you have included BOV. From a mechanistic viewpoint dose to dose
variation in absorption may be primarily due to between occasion rather than
between subject differences.
Best wishes,
Nick
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(6)62 32 46 72
email: n.holf...@auckland.ac.nz
http://holford.fmhs.auckland.ac.nz/
http://orcid.org/0000-0002-4031-2514
Read the question, answer the question, attempt all questions
-----Original Message-----
From: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of
Leonid Gibiansky
Sent: Wednesday, 12 August 2020 2:35 PM
To: Tingjie Guo <i...@tingjieguo.com>; NMusers <nmusers@globomaxnm.com>
Subject: Re: [NMusers] Random effect on ALAG between dose events
No, there is no other solution except IOV.
One option to lessen the impart of the discrepancy is to have inflated residual
error in the some interval post-dose
;TAD: time after dose
SD=THETA()
IF(TAD.LE.XX) SD=SD*THETA()
$ERROR
Y=TY*(1+SD*EPS(1))
$SIGMA
1 FIX
Then observations close to the dose (with uncertain dose time) will have less
influence on PK parameters.
Regards,
Leonid
On 8/12/2020 4:51 AM, Tingjie Guo wrote:
Dear NMusers,
I'm modeling a PK data set with a discrepancy between the documented
dosing time and the actual dosing time. According to our clinical
practice, actual dosing time is always >= documented time. I added a
ALAG with IIV to address this issue using the following formulation.
ALAG1 = THETA(5) * EXP(ETA(5))
This indeed improved the model fitting quite a lot. However, this
parameterization does not reflect the reality as I expect the ETAs
should vary between each dosing event rather than only between patients.
So I expect a "inter dose event variability" would better make sense to
this end. Since there are too many dosing events per patients, a
IOV-like approach is doable but not preferred. And it may not accurately
reflect "inter dose event variability" either. I was wondering if there
is any good solution to this problem? Any comments are very much
appreciated!
Warm regards,
Tingjie Guo
--
James G Wright PhD,
Scientist, Wright Dose Ltd
Tel: UK (0)772 5636914