Re: Model building algorithm

Date: Tue, 25 Apr 2000 07:57:01 +1200 From: Nick Holford <n.holford@auckland.ac.nz> Subject: Re: Model building algorithm LSheiner wrote: > > 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. My 2 cents -- a flexible between subject variability (BSV) model means to me having a full block OMEGA structure so that any parameter which has BSV is by default assumed to be correlated with any other parameter. The rationale for this is that NOT including the covariance implies a specific assumption that the covariance is zero. This assumption might be reasonable for the covariance between a set of PK parameters and a set of PD parameters in the same model but I would not think it reasonable a priori to have a zero covariance within the set of PK parameters or within the set of PD parameters. Note that the same idea applies to within subject variability (WSV). The commonly known example of WSV uses occasion as a covariate to identify between occasion variability (BOV) (aka IOV). An OMEGA BLOCK should be considered the default for parameters with BOV e.g. BOV in a PK model may often be due to differences in bioavailability which will be reflected in the covariance between CL/F and V/F. Failure to use an OMEGA BLOCK for BOV is the same as saying biovailability does not influence CL/F and V/F (assuming there is not a an explicit OMEGA for F in the model). > 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). Can you explain why you would use this rather than the more common model for variability in Y e.g. F = fn(time*exp(eta1))*exp(eps1) ? How do you interpret the eta on Time? If you had used an additive model e.g. TSCALE=eta then I would think of this as reflecting random error in measurement of time but I have difficulty understanding the subject specific magnitude. I find it even harder to interpret a proportional model where the error in time gets bigger the longer one waits. -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm Date: Mon, 24 Apr 2000 13:54:44 -0700