Covariate Models Using Weight

Date: Mon, 22 Nov 1999 10:58:08 +0100 From: Karin Fattinger <fattinge@kpt.unizh.ch> Subject: Covariate Models Using Weight I just would like to add, that there exists a new formula predicting renal function from serum creatinine (and other covariates) developed from 1628 patients with chronic renal disease: Levey-AS; Bosch-JP; Lewis-JB; Greene-T; Rogers-N; Roth-D, A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group, Ann-Intern-Med. 1999 Mar 16; 130(6): 461-70. Besides serum creatinine concentrations and covariates(Age, gender and race), this formula includes also serum albumin concentrations and serum urea nitrogen concentrations. Regards. Karin Division of Clinical Pharmacology and Toxicology Department of Medicine University Hospital Raemistrasse 100 CH-8091 Zuerich Switzerland Tel. + 41 1 255 2067 Fax. + 41 1 255 4411 email: fattinge@kpt.unizh.ch

From: "Stephen Duffull" <sduffull@fs1.pa.man.ac.uk> Subject: Re: Covariate Models Using Weight Date: Fri, 19 Nov 1999 09:15:41 -0000 There has been considerable discussion about covariate models etc. I perceive that the discussion has slightly wider implications. Are covariates that have a mechanistic flavour better than those that do not (or none at all)? 1) If you have a choice of two covariates (eg creatinine clearance or serum creatinine). eg. If you have a renally cleared drug and you can use either estimated creatinine clearance (eg Cockcroft and Gault or Jelliffe & Jelliffe) as a covariate or some empirical model based on serum creatinine and both models yield the same objective function with the same number of parameters then which do you use? I personally prefer the generality of the creatinine clearance equation. The same is true for Nick's argument - if an allometric scaling factor has been developed that has generality outside of the particular experiment that is being considered currently and still predicts as well as an empirical choice then I personally would prefer using it (even if I could not substantiate the value of the power (3/4) in this particular experiment). 2) If a covariate does not improve the fit of your model do you include it? eg If adding WT^(?) as a covariate for Vd or CL does not alter the objective function significantly - then do you include it as a descriptor? This depends on why you are modelling (eg descriptive or predictive). If "predictive" then it would be difficult not to believe that larger people don't have larger Vd or CL - even if you couldn't show this based on the current experimental design. I don't think it is bad to include prior beliefs, as long as they have some basis in reality, in the model building exercise and indeed if the process needs to be formalised then a Bayesian solution may be appropriate. In both examples assumptions in model building need to be transparent to the user. Just a thought Steve ===================== Stephen Duffull School of Pharmacy University of Manchester Manchester, M13 9PL, UK Ph +44 161 275 2355 Fax +44 161 275 2396

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: Covariate Models Using Weight Date: Fri, 19 Nov 1999 12:09:44 +0100 Good point, Steve. Mechanistic considerations are very useful and can guide model development. Just one example from own practice: a drug is eliminated exclusively via renal route. WT and CLCR are almost equal as predictors for CL. What to prefer? I prefer CLCR to WT since I know the mechanism of elimination. CLCR and WT are highly correlated and that is why the inclusion of WT in CL submodel works as well as CLCR. The problem of correleted predictors! Mechanistic considerations help selecting right predictors. Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

Date: Fri, 19 Nov 1999 10:09:16 -0600 From: James Gallo <JM_Gallo@fccc.edu> Subject: Re: Covariate Models Using Weight I would be hesistant to use CLCR [creatinine clearance] as a covariate for a drug that undergoes appreciable renal excretion, particularly if CRCL is estimated from a standard formula. These calculated formulas are derived [depending on the particular formula, Cockcroft-Gault, etc...] from, if my memory is correct, a relatively small populations of subjects. Moreover, its been shown that other measures [such as 51Cr-EDTA clearance] are better predictors of renal function or glomerular filtration rate than the 'calculated' formulas that suggest CRCL is of lower mechanistic value. These latter methods [51Cr-EDTA, etc..] of estimating renal function are not always readily available, however, I believe [I think someone has published on this as well] estimation of renal function/GFR by serum creatinine plus other covariates is a better predictor than the 'calculated' formulas. You also raise the issue of correlation [collinearity] amongst covariates. There are a variety of objective criteria that can indicate such a problem that you are probably much more familiar with than myself. Not to deny you artisitc freedom as a modeler, but I would like to think objective criteria can direct your selction of covariates in most cases. At the same time, if I interpret your message correctly, I agree that selection of covariates is problematic even given objective criteria. jim gallo

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: Covariate Models Using Weight Date: Sun, 21 Nov 1999 10:28:24 +0100 1. I would certainly prefer 51Cr-EDTA to CLCR, but the former is rather an exception in clinical practice. 2. I would also prefer measured CLCR to calculated. If we speak about calculated CLCR as a single parameter describing renal function, there is a lot of formulas, all based on relatively small number of subjects. I personally like the formula appeared in NONMEM Users Guide Part V, p.33 (the calculated parameter is called RF which stands for "renal function"): RF = WT*(1.66-0.011*AGE)/SCR 3. Applying any of the above mentioned formulas (Cockcroft-Gault including) is exactly what you suggest: "estimation of renal function/GFR by serum creatinine plus other covariates". Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

Date: Sun, 21 Nov 1999 10:22:07 -0600 From: James Gallo <JM_Gallo@fccc.edu> Subject: Re: Covariate Models Using Weight Possibly we agree on how renal function can be 'modeled' using covariates, however, application of pre-defined formulas, such as you indicate in points 2 and 3 [ala Cockcroft-Gault, or similar] is not what I meant by "estimation of renal function/GFR by serum creatinine plus other covariates". Such formulas would be derived uniquely for the particular drug and database. jim gallo

Date: Mon, 22 Nov 1999 10:01:47 +0000 From: James <J.G.Wright@ncl.ac.uk> Subject: CrCL, 51-CrEDTA etc. Dear Nick, Valdimir, James etc. My personal experience is that the Cockcroft & Gault formula is not very good (at least, in cancer patients, I would not recommend its use). The paper by C&G is vulnerable to criticism from many aspects (4% women, questionable methodology) I am not familiar with the paper by Bjornsson, but if it is from 1979, then we again have to be wary that creatinine assays may have changed a lot since then (and certainly have in my experience). Another problem is that we can't really know the creatinine production, or guarantee that we are in steady-state. We have done some work with surrogate markers and gained a modest improvement over the conventional covariates used (SCr, body size measure, sex, age). If we are trying to generate a surrogate for creatinine production using age, weight and gender, then this may well be population-dependent and before using the Bjornsson formula this would have to be considered careful. Perhaps we can all agree that mechanistic considerations suggest we should reciprocate serum creatinine. James PS 51Cr-EDTA clearance and creatinine clearance don't actually measure the same thing. Which we use depends on the drug (and clinical population) - its the drugs clearance that we need a predictor for (not how much creatinine is in their urine).