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
9198469185
 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 9198469185
 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)3737090 mobile: +33 64 2716369 (Apr 6Jul 17 2009) http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
<=
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