RE: General question on modeling
Steve,
I think we're in complete agreement, with one exception. You write
> 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).
No, I meant that one model suggests the dose should be 100 BID and the
other suggests it should be 200 QD. Or that the ED50 is 50 mg, and so
the dose should be (maybe) 100 mg, or the ED50 is 200, so the dose
should be (maybe) 400 mg. Which do you choose (in the real world,
commericial gets to choose, so it will be qd - and it will be a blue
pill)? While we do, in general, have tools to determine which of these
two models is "better", do we have tools that will insure that we even
have these two models to evaluate. Or, given the tools we have, are we
like to get one, and never even consider the other. Again, we have
lots of discussion about "which of these two models is better", very
little about how to find these two models to compare in the first
place. There certainly is no single criteria by which to evaluate the
models, must be purpose specific.
Mark Sale MD
Next Level Solutions, LLC
www.NextLevelSolns.com
Quoted reply history
> -------- Original Message --------
> Subject: RE: [NMusers] General question on modeling
> From: "Stephen Duffull" <[EMAIL PROTECTED]>
> Date: Mon, March 19, 2007 8:42 pm
> To: "'Mark Sale - Next Level Solutions'" <[EMAIL PROTECTED]>
> Cc: <[email protected]>
>
> 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
> --