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 > > > >
