Dear Leonid, Nick, Thank you for the suggestions and remarks. Very helpful!
Warm regards, Tingjie On Wed, Aug 12, 2020, at 18:13, 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 > > > > -- Warm regards, Tingjie Guo
