Dear Patricia,

Your infusion time will not be semantically distributed.

I suppose maximum distribution toward planned DUR. But you may have left
half of the distribution curve with maximum value of predetermined infusion
time.

So model is likely to be

D1* (1-abs(THETA(1)*EPA(1)))




On Wed, Aug 5, 2020, 12:50 PM Patricia Kleiner <pkle...@uni-bonn.de> 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
>
>
>
>

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