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=

k

applies to linear and generalized linear (i.e. categorical, etc) regression=

models, nonlinear models (and mixed effects models) would generally fall in=

to

the "well, if the simple case was like that, it can't be any simpler for th=

e

harder cases..."... Frank demonstrates some of the reasons that p-values

from models generated using stepwise modeling are fairly useless (i.e. don'=

t

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=

e,

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
*

*> www.NextLevelSolns.com
*

--

best,

-tony

blindglobe

Muttenz, Switzerland.

"Commit early,commit often, and commit in a repository from which we can

easily

roll-back your mistakes" (AJR, 4Jan05).

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

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=

k

applies to linear and generalized linear (i.e. categorical, etc) regression=

models, nonlinear models (and mixed effects models) would generally fall in=

to

the "well, if the simple case was like that, it can't be any simpler for th=

e

harder cases..."... Frank demonstrates some of the reasons that p-values

from models generated using stepwise modeling are fairly useless (i.e. don'=

t

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=

e,

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:

--

best,

-tony

blindglobe

Muttenz, Switzerland.

"Commit early,commit often, and commit in a repository from which we can

easily

roll-back your mistakes" (AJR, 4Jan05).

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