RE: FO VS FOCE VS FOCE WITH INTERACTION

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: FO VS FOCE VS FOCE WITH INTERACTION Date: Tue, 27 Feb 2001 15:43:36 +0100 Atul, John gives a nice explanation of the differences between various approximations available in NONMEM. If you have a heteroscedastic residual error model (e.g. a constant CV model: Y = F*(1+EPS(1))) FOCE with interaction is the method of choice. The only problem is that it is computationally very intensive. Sometimes it even does not work at all (especially with complex models) due to numerical problems. It is a good practice to get rid of heteroscedasticity by applying an adequate transformation to data and model predictions. In case of PK data a good transformation is a logarithmic one. In the data set, you should create a column with the natural logarithms of concentrations which will serve as DV. In the $ERROR block, you take the logarithm of the model prediction: $ERROR CALLFL=0 IPRE = -3 IF(F.GT.0) IPRE = LOG(F) ; to avoid LOG(0) run-time error Y = IPRE + EPS(1) With such a transformation, you do not need FOCE with INTERACTION anymore. Just FO usually works fine. Best regards, Vladimir