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
