Dear Patricia,
This distribution might to analogous to relative bioavailability estimate,
which is bounded between 0 to 1. Typically, we use the logit-transformation in
F1 estimate.
For example:
m1 = log(θ1/(1- θ1))
EE1 = m1 + η1
F1 = exp(EE1)/[1 +exp(EE1)]
Best regards,
Sam Liao,
Pharmax Research
> On August 5, 2020 9:18 AM Patricia Kleiner <[email protected]> wrote:
>
>
> Dear all,
>
> I am developing a PK model for a drug administered as a long-term infusion
> of 48 hours using an elastomeric pump. End of infusion was documented, but
> sometimes the elastomeric pump was already empty at this time. Therefore
> variability of the concentration measurements observed at this time is quite
> high.
> To address this issue, I try to include variability on infusion duration
> assigning the RATE data item in my dataset to -2 and model duration in the
> PK routine. Since the "true" infusion duration can only be shorter than the
> documented one, implementing IIV with a log-normal distribution
> (D1=DUR*EXP(ETA(1)) cannot describe the situation.
>
> I tried the following expression, where DUR ist the documented infusion
> duration:
>
> D1=DUR-THETA(1)*EXP(ETA(1))
>
> It works but does not really describe the situation either, since I expect
> the deviations from my infusion duration to be left skewed. I was wondering
> if there are any other possibilities to incorporate variability in a more
> suitable way? All suggestions will be highly appreciated!
>
>
> Thank you very much in advance!
> Patricia
