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RE: OMEGA selection

From: Mark Sale - Next Level Solutions <mark>
Date: Thu, 23 Apr 2009 08:50:39 -0700
Yaming,
  For details, I'd refer you to the abstract= s, I've never published this.  But, whenever I do a bootstrap I look a= t whether the samples that had a successful covariance step are different (= in mean or variability), just for my own interest.  They never have be= en different, I'd guess I've looked at 6 or so.  I have no records of = what fraction of samples had a successful covariance step.
I'd also refe= r to any number of good reference on how to decide if a model is "good" (pl= ots, biological plauability, reasonable parameters, various metrics of "goo= dness". etc.  I'd suggest that if your parameters are poorly defined b= y the data (e.g., all concentrations near EMAX, unable to define EC50) you'= ll invariably find that other metrics suggest lack of model goodness. = Whether and how successful covariance or minimization fits into this will = have to wait until we have a universally accepted metric of model "goodness= ".
I would list CI (based on bootstrap, not $COV) among my metrics of mo= del goodness, I'd even list a successful covariance step among metrics of m= odel goodness - but pretty far down the list. (everything else being equal,= I'd prefer a model that has a successful covariance step - of course every= thing else is never equal).


Mark Sale MD
Next Level Solutions, LLC
www.NextLevelSolns.com
919-846-9185

-------- Original Message --------
Subject: RE: [NMusers] OMEGA selection
From: Hang, Yaming <yaming_hang Date: Thu, April 23, 2009 10:35 am
To: "Mark Sale - Next Level Solutions" <mark Cc: <nmusers
= Hi Mark,
 
Very inter= esting 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 qu= estions about why such a conclusion was reached. My questions are: 1. = how many data sets are bootstrapped, 2. among them, what's the frequen= cy of failed vs. successful covariance step, 3. are parameter estimates the= mselves similar across different bootstraps, 4. are there any major di= fference among the data sets leading to successful and failed covariance st= ep?
 
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 concent= ration) and the other with ill distribution. So that the first da= ta set gives me a successful run including $COV step with re= asonable estimates for Emax and EC50, the second data set will lead to a&nb= sp;total failure in estimation, even estimates for Emax and EC50 canno= t be obtained. I guess I cannot use this as a basis to conclude that even t= he $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

Nick et al.
    At th= is risk of starting an discussion that probably has little mileage left in = it.  First I agree with Nick on covariance - it probably doesn't matte= r.  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.&= nbsp; Our evidence is that, when bootstrapping, the parameters for the samp= le that have successful covariance are not different from those that failed= .  So, we conclude that the results are the same regardless of covaria= nce outcome across sampled data sets - the independent variable in this tes= t is the data set, the model is fixed.
In model selection/building, we h= ave a fixed data set and the independent variable is the model structure.&n= bsp;  Whether covariance success is a useful predictor across differen= t models with a fixed data set is a different question than whether covaria= nce is a useful predictor across data sets with a fixed model.
But, in t= he end, I do agree that biological plausibility, diagnostic plots, reasonab= le 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 select= ion
From: Nick Holford <n.holford ril 15, 2009 12:17 pm
To: nmusers 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
opi= nion is not supported experimentally e.g. see

NONMEM has no idea if the parameters make= sense or not and will happily
converge with models that are overparame= terised. You cannot rely on a
failed $COV step or a MINIMIZATION TERMIN= ATED message to conclude the
model is not a good one. You need to use y= our brains (NONMEM does not
have a brain) and your common sense to deci= de if your model makes sense
or is perhaps overparameterised.

Ni= ck

Ethan Wu wrote:
>
> Dear all,
>
> I am fi= tting 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 goo= d principle of Eta reduction that I could
> implement here. Any good= reference?
>
>

--
Nick Holford, Dept Pharmacology &= amp; Clinical Pharmacology
University of Auckland, 85 Park Rd, Private B= ag 92019, Auckland, New Zealand
n.holford 730 fax:+64(9)373-7090
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 - 11:50:39 EDT

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