NONMEM Users Network Archive

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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= Sale MD
Next Level Solutions, LLC

-------- Original Message --------
Subject: Re: [NMusers] OMEGA selection
From: Nick Holford <n.holford Date: Wed, April 15, 2009 12:17 pm
To: nmusers

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 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.


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)

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

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