RE: model for OMEGA and SIGMA

From:VPIOTROV@PRDBE.jnj.com Subject: RE: [NMusers] model for OMEGA and SIGMA Date: Fri, 7 Feb 2003 21:59:08 +0100 Luciane, Bill is right saying that the error structure should reflect somehow your data. All PK parameters are positive, and by coding interindividual variability like CL=THETA(.)*EXP(ETA(.) and by using FOCE method we constrain CL to be positive. Similarly, concentration is positive, and the way to constrain it could be Y=F*EXP(EPS(1)). However, due to model linearization, NONMEM will treat this as Y=F*(1+EPS(1)). In order to properly constrain the model prediction you have to apply a so-called tranform-both-side approach by taking the logarithm of measured concentrations (DV variable in your data set) and of model prediction. In the log domain the exponential residual error becomes additive. The $ERROR block may look as follows: $ERROR IPRE = -5 ; arbitrary value; to prevent from run stop due to log domain error IF (F.GT.0) IPRE = LOG(F) ; note: in FORTRAN, LOG() means natural logarithm, not decimal! Y = IPRE + EPS(1) BTW, the magnitude of SIGMA depends not only on the assay error. Nevertheless, if you know the precision of the bioanalytical method decreases as concentration drops below a certain level you may consider the model with 2 EPS. Best regards, Vladimir