RE: CCV or Additive error models?
Dear Yuanyue,
If this is a standard PK analysis I would first like to investigate what a
simple CCV model does: Y=F+F*ERR(1) or Y=F*EXP(ERR(1)), as this would seem
to me the default for any model derived on concentration data using standard
assay technology.
If you know you have low epsilon shrinkage (look for the line saying
EPSshrink(%) in your output, and trust Mats and Rada* that you need to stay
well below 20%) you could make a diagnostic plot of the absolute values of
your individual weighted residuals against PRED or IPRED. If you have what
could be judged as a horizontal smooth through the data, your model is fine.
If you have pronounced higher values at the low end when you use a constant
CV model, you add on the additive component. My guess would be that with
your additive-only model you would see an increase in your IWRES values with
increasing PRED/IPRED.
No matter what Nick says, I still prefer a succesful covariance step, as
lack of one can be an indication of an over-parameterised model. I do know
that almost all the biologically relevant models struggle with
over-parameterisation, but given a choice I would rather not
(unnecessarily?) have this flaw in my residual error model.
Cheers,
Rik
*MO Karlsson and RM Savic. Diagnosing model diagnostics. CPT 2007
82:1:17-20.
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Nick Holford
Sent: 01 July 2011 6:11 PM
To: nmusers
Subject: Re: [NMusers] CCV or Additive error models?
Yuanyue,
It is has been well established and discussed at length on nmusers that a
successful $COV step says nothing about the suitability of the model to
describe the data.
On the other hand the OFV is a very reliable statistic for model selection.
You should use the combined error model.
Nick
On 1/07/2011 5:17 p.m., [email protected] wrote:
> Dear nmusers,
>
> In one of my PK analysis, if I chose Y=F+F*ERR(1)+ERR(2), I got lower
> objective value=1130.907, but $COV step failed (S matrix is singular);
> when I chose Y=F+ERR(1), I got $COV successful but higher objective
> value=1351.735. I prefer to choose the addivtive error model even if
> with higher objective value because it seems comfortable to see $COV
> successful. Can anyone give me other suggestions?
>
> Thanks
>
>
> Yuanyue (Paul) Gao
>
> School of Pharmacy
> University of Pittsburgh
> 716 Salk Hall
> 3501 Terrace Street
> Pittsburgh, PA 15261
> Phone: 412-648-8546
> E-mail: [email protected]
>
--
Nick Holford, Professor Clinical Pharmacology Dept Pharmacology& Clinical
Pharmacology 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