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RE: OMEGA selection

From: Hang, Yaming <yaming_hang>
Date: Thu, 23 Apr 2009 10:35:07 -0400

Hi Mark,
 
Very interesting point. In general, your logic about why the covariance
step doesn't matter in the bootstrapping case makes sense to me.
However, I have some questions about why such a conclusion was reached.
My questions are: 1. how many data sets are bootstrapped, 2. among them,
what's the frequency of failed vs. successful covariance step, 3. are
parameter estimates themselves similar across different bootstraps, 4.
are there any major difference among the data sets leading to successful
and failed covariance step?
 
I am imagining an example: with an Emax model, I generate two data sets,
one with good distribution with regard to the X variable (say
concentration) and the other with ill distribution. So that the first
data set gives me a successful run including $COV step with reasonable
estimates for Emax and EC50, the second data set will lead to a total
failure in estimation, even estimates for Emax and EC50 cannot be
obtained. I guess I cannot use this as a basis to conclude that even the
$ESTIMATE step is not reliable, since both data sets are coming from the
same population, right?
 
I'd love to hear your thoughts on this one.
 
Thanks,
Yaming

________________________________

From: owner-nmusers
On Behalf Of Mark Sale - Next Level Solutions
Sent: Wednesday, April 15, 2009 1:00 PM
Cc: nmusers
Subject: RE: [NMusers] OMEGA selection


          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
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
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
> $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
        
        
        

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Received on Thu Apr 23 2009 - 10:35:07 EDT

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