Re: RE: Proper way to handle the pre-first dose PK observation for non-endogenous drug
Yaming, Matt,
I would do exactly what Yaming has done already. Treat the pre-dose measurements as true observations for when the predicted conc is zero.
It is not true to say they provide no information about model parameters. They are the best way to improve your estimate of the additive error parameter (independent of PK model misspecification). By improving the residual error model you may also have benefits in improving your PK model. Although the PK model benefit may be small in principle it is foolish to ignore data that could be helpful.
A major weakness of using log transformed both sides approach is that it cannot use these real observations which is why I have rarely used it.
Best wishes,
Nick
Quoted reply history
On 8/11/2012 11:23 a.m., Fidler,Matt,FORT WORTH,R&D wrote:
> Yaming,
>
> As you pointed-out DV=Prediction. Including these data-points biases your estimate of the additive component of variability. My opinion is just to exclude the observations to get a better estimate of additive variability.
>
> On a side note Additive+Proportortional is similar to a lognormal-error structure. In a lognormal error structure zero observations have to be excluded anyway.
>
> Matt.
>
> *From:* [email protected] [ mailto: [email protected] ] *On Behalf Of *Yaming Hang
>
> *Sent:* Wednesday, November 07, 2012 4:04 PM
> *To:* [email protected]
>
> *Subject:* [NMusers] Proper way to handle the pre-first dose PK observation for non-endogenous drug
>
> Dear NONMEM Users,
>
> I’d like to get some advice from you with regard to how to handle the pre-first 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. There were very little difference for all parameters except for the SD of the additive error. When these pre-first dose concentrations were treated as missing, 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 parameters 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 exactly 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 your experience/insight.
>
> Yaming Hang, Ph.D.
>
> Pharmacometrics
>
> Biogen Idec
>
> 14 Cambridge Center
>
> Cambridge, MA 02142
>
> Office: 781-464-1741
>
> Fax: 617-679-2804
>
> Email: [email protected] <mailto:[email protected]>
>
> ------------------------------------------------------------------------
>
> This e-mail (including any attachments) is confidential and may be legally privileged. If you are not an intended recipient or an authorized representative of an intended recipient, you are prohibited from using, copying or distributing the information in this e-mail or its attachments. If you have received this e-mail in error, please notify the sender immediately by return e-mail and delete all copies of this message and any attachments.
>
> Thank you.
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford