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RE: General question on modeling

From: Mark Sale - Next Level Solutions <mark>
Date: Tue, 20 Mar 2007 06:09:30 -0700

  Beg to differ, but ...
 In all other sciences being able to independently reproduce results is
the hallmark of a valid piece of work. (remember cold fusion?, not one
else could reproduce it, invalid, then there was angiogenic factors, no
one else could reproduce (for a long time), then when Folkman showed
people how, it was valid). Why are we so special that it is OK for the
same experiement to give different results- even different conclusions,
and both are valid? It think this is just more than differences in
interpreting data - it's like two people do a T test and get different
answers. It that happens, we need to question whether the T test is a
valid method.
 But, I agree that covariates are a fairly trivial contributor to
explaining variability. The biggest contributor to variability is time
(high concentration just after dose, low long after dose). So, usually
it is the structural model that drives pretty much everything. It
matters more if you chose an Emax over an indirect response for your PD
model than whether you put age as a predictor of Emax.


Mark Sale MD
Next Level Solutions, LLC

> -------- Original Message --------
> Subject: [NMusers] General question on modeling
> From: "Bonate, Peter" <Peter.Bonate
> Date: Tue, March 20, 2007 8:20 am
> To: <nmusers
> Sometimes these threads kill me. There is a degree of art to modeling.
> The art is the intangible things that we do during model development.
> If there was no art, if it was all based on science, then all modelers
> would be equal and two modelers would always come to the same model.
> The fact that we don't is the uniqueness of the process and therein lies
> the art.
> I would also like to argue that for most drugs, covariate inclusion in a
> model often reduces BSV and residual variability by very little. There
> are very few magic bullet covariates like GFR with aminoglycosides. I
> would think that if two experienced modelers analyzed the same data set
> and came up with different models that if we were to examine these
> models we would find they probably would have similar predictive
> performance. A classic example of this is when you do all possible
> regressions with a multiple linear regression model.
> Pete bonate
> Peter Bonate, PhD, FCP
> -----Original Message-----
> From: owner-nmusers
> To: 'Mark Sale - Next Level Solutions' <mark
> CC: nmusers
> Sent: Mon Mar 19 19:42:18 2007
> Subject: RE: [NMusers] General question on modeling
> Mark
> > But, I have to admit that I'm uncomfortable with the concept
> > of the "art" of modeling.
> I agree - I like to think of it as a science of modelling - but I have
> heard
> (at conferences) the "science" of modelling referred to as the "art" of
> modelling.
> > decisions on art? Shouldn't we be striving for something
> > more objective than art?
> We have that now. The model should perform well in the area that it's
> supposed to. There are a number of diagnostic and evaluation techniques
> that one can use to ask the question "Is my model any good for the
> purpose
> for which I built it?". I think the underlying concept of striving for
> a
> single method for building models is inherently flawed.
> > If this is art, how do we deal with
> > the reality that two modelers will get different answers (I
> > know,... neither of which is right), but in the end we do
> > need to recommend only one dosing regimen.
> By different answers - are you referring to different models? In which
> case
> the models would presumably be sufficiently confluent that their
> predictions
> of the substantive inference (e.g. dosing regimen) would be the same or
> at
> least very similar (to within an acceptable dose size).
> IMHO, a mistake is made in drug development when we try and find the
> best
> single model at every stage of the process. Why not have a selection of
> plausible models which all provide essentially the same inferences. In
> this
> case when we design the next study our design will incorporate a
> quantitative measure of our uncertainty in the model, rather than just
> saying - "this is the model and that's that".
> > You suggest (I think) that we should select our model based
> > on what inference we want to examine. I agree. But that is
> > not the question either. There are volumes written about how
> > to identify the best/better model once you've found it. I'm
> > interest in how we find it.
> This is my point exactly - I don't believe there is an absolute, linear
> method available for finding the best model within the framework of
> hierarchical nonlinear models (there - I've said it).
> Steve
> --
Received on Tue Mar 20 2007 - 09:09:30 EDT

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