Re: Random effect on ALAG between dose events
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
Quoted reply history
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: [email protected]
> 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: [email protected] <[email protected]> On Behalf Of
> Leonid Gibiansky
> Sent: Wednesday, 12 August 2020 2:35 PM
> To: Tingjie Guo <[email protected]>; NMusers <[email protected]>
> 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