General question on modeling
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
Quoted reply history
-----Original Message-----
From: [EMAIL PROTECTED] <[EMAIL PROTECTED]>
To: 'Mark Sale - Next Level Solutions' <[EMAIL PROTECTED]>
CC: [email protected] <[email protected]>
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
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