Re: [NMusers] 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 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/-0002-4031-2514 Read the question, answer the question, attempt all questions -Original Message- From: owner-nmus...@globomaxnm.com On Behalf Of Leonid Gibiansky Sent: Wednesday, 12 August 2020 2:35 PM To: Tingjie Guo ; NMusers 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
Re: [NMusers] Random effect on ALAG between dose events
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/-0002-4031-2514 > Read the question, answer the question, attempt all questions > > -Original Message- > From: owner-nmus...@globomaxnm.com On > Behalf Of Leonid Gibiansky > Sent: Wednesday, 12 August 2020 2:35 PM > To: Tingjie Guo ; NMusers > 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
RE: [NMusers] Random effect on ALAG between dose events
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/-0002-4031-2514 Read the question, answer the question, attempt all questions -Original Message- From: owner-nmus...@globomaxnm.com On Behalf Of Leonid Gibiansky Sent: Wednesday, 12 August 2020 2:35 PM To: Tingjie Guo ; NMusers 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 >
RE: [NMusers] Random effect on ALAG between dose events
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 patients 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/-0002-4031-2514 Read the question, answer the question, attempt all questions -Original Message- From: owner-nmus...@globomaxnm.com On Behalf Of Leonid Gibiansky Sent: Wednesday, 12 August 2020 2:35 PM To: Tingjie Guo ; NMusers 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 >
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
[NMusers] Random effect on ALAG between dose events
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
