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RE: Proper way to handle the pre-first dose PK observation for non-endogenous drug

From: Fidler,Matt,FORT WORTH,R&D <Matt.Fidler>
Date: Wed, 7 Nov 2012 16:23:47 -0600


As you pointed-out DV=Prediction. Including these data-points biases you=
r estimate of the additive component of variability. My opinion is just =
to exclude the observations to get a better estimate of additive variabilit=

On a side note Additive+Proportortional is similar to a lognormal-error str=
ucture. In a lognormal error structure zero observations have to be exclud=
ed anyway.


From: owner-nmusers
 Behalf Of Yaming Hang
Sent: Wednesday, November 07, 2012 4:04 PM
To: nmusers
Subject: [NMusers] Proper way to handle the pre-first dose PK observation f=
or non-endogenous drug

Dear NONMEM Users,

I'd like to get some advice from you with regard to how to handle the pre-f=
irst dose PK observation when the drug is not an endogenous substance.

I tried too different approaches, one approach is treating them as missing =
values (DV=0, EVID=0, MDV=1), another is treating them as true 0s (DV=
=0, EVID=0, MDV=0). My error structure is proportional + additive. Th=
ere were very little difference for all parameters except for the SD of the=
 additive error. When these pre-first dose concentrations were treated as m=
issing, the estimated omega for additive error is 3.92, and when they were =
treated as true 0s, the sigma became 2.85.

To me, in theory, these values provide no information about the model param=
eters because the system will predict them to be 0 at time 0 anyway for any=
 point in the parameter space. Is what happened here that because DV is exa=
ctly the same as prediction, therefore the estimation of additive residual =
error variance has been brought down?

Which way is more appropriate? I'd really appreciate it if you can share yo=
ur experience/insight.

Yaming Hang, Ph.D.
Biogen Idec
14 Cambridge Center
Cambridge, MA 02142
Office: 781-464-1741
Fax: 617-679-2804
Email: Yaming.Hang

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Thank you.

Received on Wed Nov 07 2012 - 17:23:47 EST

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