General question on modeling

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