Re: General question on modeling
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 book
applies to linear and generalized linear (i.e. categorical, etc) regression
models, nonlinear models (and mixed effects models) would generally fall into
the "well, if the simple case was like that, it can't be any simpler for the
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 Hastie,
Tibshirani, and their collaborators and former grad students (LASSO, LARS,
elastic nets, and similar approaches).
Quoted reply history
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
[EMAIL PROTECTED]
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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