RE: Your suggestions/thoughts needed on allometric base or final model

Hi Perhaps it is worth noting at this point that (as we know) not all covariates are equal. Nick indicated that some covariates are fundamental and hence should not be discarded or even tested for. Perhaps a slightly more generic framework is to consider that covariates naturally align to a hierarchical framework, for instance I think it is indisputable in this community that dose and time are at the most important covariates and we wouldn't want to construct a PK model without them. Other covariates, such as extracorporeal elimination (when relevant) are likely to be of a higher order of importance than covariates based on phenotype, then biologically meaningful covariates such as renal function, allometric weight, lean body weight are more important than empirical covariates such as age... And so forth. So, when adding covariates into the model the hierarchy in which they enter should, under a bioligical framework, not necessarily be up for statistical testing. Since all of our models are nonlinear and therefore most linear operations such as forward addition/back elimination are not consistent then we really have no choice but to construct our hierarchy and consider tests for covariates from the same hierarchical level (but perhaps not between them). So under this construct you wouldn't consider swapping dose for weight if it turned out that weight was statistically better than dose and you also wouldn't consider swapping weight for age if age was statistically better ... At what stage you consider a covariate to be "fundamental" meaning it should never be excluded will depend on the circumstance. Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: [EMAIL PROTECTED] P: +64 3 479 5044 F: +64 3 479 7034 Design software: www.winpopt.com