Just realized the typical value of this estimate cannot be 1.0. You may need other transformation.
Sam > On August 5, 2020 9:59 AM Sam Liao <sl...@pharmaxresearch.com> wrote: > > > 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 <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
