Re: Model building algorithm

Date: Fri, 21 Apr 2000 13:18:07 -0700 From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: Model building algorithm All- I don't think there is an official "statistical" opinion on this - indeed, the idea of starting with the biggest possible model and then pruning it is probably more consistent with statistical "theory" than building up from a small model. The problem with the backward-elimination-only strategy is usually practical: running into rounding errors, etc. I think the injunctions you have heard about building the structural model first were not stated as they should have been: the idea is to create the structural model in a context of a flexible inter-indivdiuual variance model, so Bill's idea of putting etas on everything goes along with that philosophy. Indeed, I think we have all seen cases where doing what Bill saqys, but limiting OMEGA to be strictly diagonal has led to problems in model building. I generally now-a-days, use a 2 x 2 OMEGA while building my regression model, one eta scales Y (i.e., Y = F*(1+eta) or F*EXP(eta)) and one eta scales X (usually time ... This is implemented as TSCALE = exp(eta), where TSCALE is allowed). Effectively, then the generic variability model is F = fn(time*exp(eta1))*exp(eta2). I can't guarantee that this will help Mark's problem ... perhaps he can tell us whether, if he uses this structure for eta, NONMEM can "see" the biexponentiality of his data better ... L. _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Bioph. Sci., Med. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)