Re: CrcL or Cr in pediatric model

Jakob, Restrictions on the parameter values is not the only (and not the major) problem with additive parametrization. In this specific case, CRCL (as clearance) increases proportionally to WT^(3/4) (or similar power, if you accept that allometric scaling has biological meaning or that the filtration rate is proportional to the kidney size). Then you have TVCL=THETA(1)*WT^(3/4)+THETA(2)*WT^(3/4) (where the second term approximates CRCL dependence on WT). Clearly, the model is unstable. Answering the question: > why would two persons, with WT 50 and 70 kg > but otherwise identical (including CRCL and any other covariates, > except WT), be expected to differ by 36% in CL? we are back to the problem of correlation. If two persons of different WT have the same CRCL, they should differ by the "health" of their renal function. I would rater have the model CL=THETA(1)*(WT/70)^(3/4)*(CRCL/BSA)^GAMMA Then, if two subjects (50 and 70 kg) have the same CRCL, their CL will be influenced by WT, and by renal function (in this particular realization, CRCL per body surface area). While the result could be the same as in CL ~ CRCL, we described two separate and important dependencies: CL ~ WT; and CL ~ renal function For the patient that you mentioned, they act in the opposite directions and cancel each other, but it is important to describe both dependencies. > Regarding 3 below, is the suggestion to estimate > independent allometric > models on CL for each level of renal function? The suggestion was to define the renal disease as categorical variable, and then correct CL, for example: TCL ~ THETA(1) (for healthy) TCL ~ THETA(2) (for patients with severe renal impairment) Thanks Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Ribbing, Jakob wrote: > Leonid, > > I usually prefer multiplicative parameterisation as well, since it is > easier to set boundaries (which is not necessary for power models, but > for multiplicative-linear models). However, boundaries on the additive > covariate models can still be set indirectly, using EXIT statements (not > as neat as boundaries directly on the THETAS, I admit). > > In this case it may possibly be more mechanistic using the additive > parameterisation: For example if the non-renal CL is mainly liver, the > two blood flows run in parallel and the two elimination processes are > independent (except there may be a correlation between liver function > and renal function related to something other than size). A > multiplicative parameterisation contains an assumed interaction which is > fixed and in this case may not be appropriate. If the drug is mainly > eliminated via filtration, why would two persons, with WT 50 and 70 kg > but otherwise identical (including CRCL and any other covariates, except > WT), be expected to differ by 36% in CL? This is what you get using a > multiplicative parameterisation. The fixed interaction may also drive > the selection of the functional form (e.g. a power model vs a linear > model for CRCL on CL). I do not know anything about Peter's specific > example so this is just theoretical. > > Regarding 3 below, is the suggestion to estimate independent allometric > models on CL for each level of renal function? > > Thanks > > Jakob >