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Re: General question on modeling

From: AJ Rossini <blindglobe>
Date: Mon, 19 Mar 2007 20:34:13 +0100

I'd highly recommend reading Frank Harrell's book on Regression Modeling if=
you think that stepwise regression makes any sense. While much of the boo=
applies to linear and generalized linear (i.e. categorical, etc) regression=
models, nonlinear models (and mixed effects models) would generally fall in=
the "well, if the simple case was like that, it can't be any simpler for th=
harder cases..."... Frank demonstrates some of the reasons that p-values
from models generated using stepwise modeling are fairly useless (i.e. don'=
follow the behavior you'd expect from p-values).

The literature to start looking at would be modern variable selection
techniques for linear regression, i.e. work at Stanford Statistics by Hasti=
Tibshirani, and their collaborators and former grad students (LASSO, LARS,=
elastic nets, and similar approaches).

On Monday 19 March 2007 19:32, Mark Sale - Next Level Solutions wrote:
> Dear Colleagues,
> I've lately been reviewing the literature on model building/selection
> algorithms. I have been unable to find any even remotely rigorous
> discussion of the way we all build NONMEM models. The structural
> first, then variances/forward addition/backward elimination is
> generally mentioned in a number of places (Ene Ettes in Ann
> Pharmacother, 2004, Jaap Mandemas series on POP PK series J PK Biopharm
> in 1992, Jose Pinheiros paper from the Joint Stats meeting in 1994,
> Peter Bonates AAPS journal article in 2005, Mats Karlsons AAPS
> PharmSci, 2002, the FDA guidance on Pop PK). It is most explicitly
> stated in the NONMEM manuals (Vol 5, figure 11.1) - without any
> reference. From the NONMEM manuals it is reproduced in many courses,
> and has become axiomatic. I've looked at the stats literature on
> forward addition/backwards elimination in both linear and logistic
> regression, where it is at least formally discussed (with some
> disagreement about whether it is "correct"). But, I am unable to find
> any justification for the structural first, then covariates (drive by
> post-hoc plots), then variance effects approach we use (I'm sure many
> people will point out that it is not nearly that linear a process,
> although in figure 11.1, Vol 5 of the NONMEM manuals, it is depicted as
> a step-by-step algorithm, without any looping back). Can anyone point
> me to any rigorous discussion of this model building strategy?
> Mark Sale MD
> Next Level Solutions, LLC


Muttenz, Switzerland.
"Commit early,commit often, and commit in a repository from which we can
roll-back your mistakes" (AJR, 4Jan05).

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Received on Mon Mar 19 2007 - 15:34:13 EDT

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