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Re: OMEGA selection

From: Nick Holford <n.holford>
Date: Wed, 15 Apr 2009 20:49:11 +0200

Mark,

I agree with your logic. In the meantime I will ignore the $COV step (it
rarely happens for me) and wait for some empirical evidence that the
$COV step is of demonstrable value for model building. Perhaps your grid
computing system could take on that challenge by comparing the results
of automated model building with and without $COV or convergence?

Nick

Mark Sale - Next Level Solutions wrote:
>
> Nick et al.
> At this risk of starting an discussion that probably has little
> mileage left in it. First I agree with Nick on covariance - it
> probably doesn't matter. But, I'd like to point out what may be an
> error in our logic.
> We content that we have demonstrated that covariance doesn't matter.
> Our evidence is that, when bootstrapping, the parameters for the
> sample that have successful covariance are not different from those
> that failed. So, we conclude that the results are the same regardless
> of covariance outcome across sampled data sets - the independent
> variable in this test is the data set, the model is fixed.
> In model selection/building, we have a fixed data set and the
> independent variable is the model structure. Whether covariance
> success is a useful predictor across different models with a fixed
> data set is a different question than whether covariance is a useful
> predictor across data sets with a fixed model.
> But, in the end, I do agree that biological plausibility, diagnostic
> plots, reasonable parameters and some suggestion of numerical
> stability/identifiably (such as bootstrap CIs) are more important than
> a successful covariance step.
>
> Mark
>
> Mark Sale MD
> Next Level Solutions, LLC
> www.NextLevelSolns.com <http://www.NextLevelSolns.com>
> 919-846-9185
>
> -------- Original Message --------
> Subject: Re: [NMusers] OMEGA selection
> From: Nick Holford <n.holford
> Date: Wed, April 15, 2009 12:17 pm
> To: nmusers
>
> Ethan,
>
> Do not pay any attention to whether or not the $COV step runs or
> even if
> the run is 'SUCCESSFUL' to conclude anything about your model. Your
> opinion is not supported experimentally e.g. see
> http://www.mail-archive.com/nmusers
> discussion and references.
>
> NONMEM has no idea if the parameters make sense or not and will
> happily
> converge with models that are overparameterised. You cannot rely on a
> failed $COV step or a MINIMIZATION TERMINATED message to conclude the
> model is not a good one. You need to use your brains (NONMEM does not
> have a brain) and your common sense to decide if your model makes
> sense
> or is perhaps overparameterised.
>
> Nick
>
> Ethan Wu wrote:
> >
> > Dear all,
> >
> > I am fitting a PD response, and the equation goes like this:
> >
> > total response = baseline+f(placebo response) +f(drug response)
> >
> > first, I tried full omega block, and model was able to converge, but
> > $COV stop failed.
> >
> > To me, this indicates that too many parameters in the model. The
> > structure model is rather simple one, so I think probably too
> many Etas.
> >
> > I wonder is there a good principle of Eta reduction that I could
> > implement here. Any good reference?
> >
> >
>
> --
> Nick Holford, Dept Pharmacology & Clinical Pharmacology
> University of Auckland, 85 Park Rd, Private Bag 92019, Auckland,
> New Zealand
> n.holford
> mobile: +33 64 271-6369 (Apr 6-Jul 17 2009)
> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
>
>

--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
n.holford
mobile: +33 64 271-6369 (Apr 6-Jul 17 2009)
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

Received on Wed Apr 15 2009 - 14:49:11 EDT

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