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Re: Variability on infusion duration

From: Sam Liao <sliao>
Date: Wed, 5 Aug 2020 10:14:44 -0700 (PDT)

Just realized the typical value of this estimate cannot be 1.0. You may nee=
d other transformation.

Sam
> On August 5, 2020 9:59 AM Sam Liao <sliao
>
>
> Dear Patricia,
> This distribution might to analogous to relative bioavailability estimate=
, which is bounded between 0 to 1. Typically, we use the logit-transformati=
on 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 <pklei05
> >
> >
> > Dear all,
> >
> > I am developing a PK model for a drug administered as a long-term infus=
ion
> > of 48 hours using an elastomeric pump. End of infusion was documented, =
but
> > sometimes the elastomeric pump was already empty at this time. Therefor=
e
> > variability of the concentration measurements observed at this time is =
quite
> > high.
> > To address this issue, I try to include variability on infusion duratio=
n
> > 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 exp=
ect
> > the deviations from my infusion duration to be left skewed. I was wonde=
ring
> > if there are any other possibilities to incorporate variability in a mo=
re
> > suitable way? All suggestions will be highly appreciated!
> >
> >
> > Thank you very much in advance!
> > Patricia
Received on Wed Aug 05 2020 - 13:14:44 EDT

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