RE: Suspicious CL/F vs. CLCR and SGOT values

From: "Janet R Wade" <jwade@pharsight.com> Subject: RE: Suspicious CL/F vs. CLCR and SGOT values Date: Tue, 1 May 2001 23:37:19 +0200 Hi Matt Some time ago (1994! - time flies!) I did some work with Nancy Sambol and Stuart Beal on the interaction between the structural (one vs. two compartment), statistical (variability) and the covariate model in a situation very similar to what you describe. I found that if I fit a one compartment model to sparse data simulated from a two compartment model then I got artifactual covariate effects and a more complex statistical model in the one compartment model (JPB, Vol 22 p165-177). I also tried a couple of real data examples where the data were so sparse as to not support using the a priori known two compartment model and again found covariates in the one compartment model that were not included when a two compartment model was fit to the data. The situation with real data is hard to pin down to spurious effects since we never really know the truth. In my case the covariate effects in the one comp model did seem reasonable. One of our conclusions was that any covariate effect that is to be included in the model should be biologically plausible. This would seem to be good advice in your situation since including creatinine clearance for a drug that is eliminated metabolically does not seem to be biologically plausible (unless that covariate is a surrogate for something else, age for example). I would suggest that you rerun your model using a two compartment structural model (fix some parameter values to a priori values if necessary) and retest to see if the covariates you found with the one compartment model are still significant. A last thing to bear in mind is to look at the size of the effect that your covariates have in the one compartment situation, if the effect is not clinically relevant then maybe you don't want to retain those covariates in the model anyway (but that does depend on what you want to do with the results of your analysis). I hope this is of some help, Janet Wade