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.holf...@auckland.ac.nz>
Date: Wed, April 15, 2009 12:17 pm
To: nmusers@globomaxnm.com
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@globomaxnm.com/msg00454.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
> $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.holf...@auckland.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090
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.holf...@auckland.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090
mobile: +33 64 271-6369 (Apr 6-Jul 17 2009)
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
