From: Bonate, Peter <*Peter.Bonate*>

Date: Tue, 20 Mar 2007 07:20:28 -0500

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 - 08:20:28 EDT

Date: Tue, 20 Mar 2007 07:20:28 -0500

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

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.

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.

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".

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 - 08:20:28 EDT