Re: General question on modeling
A lot of ink has been shed over the contrast between art and science. E.g., to what extent should model building be characterized as art? The concept "art" suggests "subjectivity", and clearly there is a subjective element to most model building. Unfortunately, the concept "art" also suggests "arbitrary preference". Most scientists probably do not consider their preferences to be arbitrary. What is missing is the concept of informed preference, or even informed instinct, which may differ across individuals, and may give contrasting yet equally valid results. For clarity, we could call this "skill". It results from training and experience, yet involves a type of knowledge that is difficult to articulate. Philospher Michael Polanyi referred to this sort of knowledge as "the tacit dimension". In his book by the same title, he championed the idea that "we know more than we can tell".
Tim Bergsma, Ph.D.
Bonate, Peter wrote:
> 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
> --