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RE: OMEGA selection

From: Mark Sale - Next Level Solutions <mark>
Date: Wed, 15 Apr 2009 10:00:06 -0700
Nick et al.
    At this risk of starting a= n discussion that probably has little mileage left in it.  First I agr= ee with Nick on covariance - it probably doesn't matter.  But, I'd lik= e 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 successf= ul covariance are not different from those that failed.  So, we conclu= de that the results are the same regardless of covariance outcome across sa= mpled data sets - the independent variable in this test is the data set, th= e model is fixed.
In model selection/building, we have a fixed data set = and the independent variable is the model structure.   Whether co= variance success is a useful predictor across different models with a fixed= data set is a different question than whether covariance is a useful predi= ctor across data sets with a fixed model.
But, in the end, I do agree th= at biological plausibility, diagnostic plots, reasonable parameters and som= e suggestion of numerical stability/identifiably (such as bootstrap CIs) ar= e more important than a successful covariance step.

Mark

Mark= Sale MD
Next Level Solutions, LLC
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 sg00454.html for
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 <= br> > $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 Eta= s.
>
> 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 Zealan= d
n.holford mobile: +33 64 271-6369 (Apr 6-Jul 17 2009)
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford


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Received on Wed Apr 15 2009 - 13:00:06 EDT

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