From: r.vanhest@erasmusmc.nl Subject: [NMusers] increasing ETA estimates with adding full block Date:8/18/2003 2:29 AM Dear NONMEM users, In having built a two compartment model with first order absorption, lag time and several covariates on V2 and CL, I ran into the following problem. When adding a full BLOCK in the OMEGA matrix to the final model the objectiove function significantly decreases (more than 100 points). However, the estimates for ETA (IIV and BOV) clearly increase. For example the ETA for KA increases from 111% to 126% and the ETA for V2 from 82% to 101%. So I seem to explain less inter-subject variability with adding a full block. Does someone has an explanation for this and knows how to deal with this? Thanks in advance, Reinier van Hest Research Pharmacist Department of Pharmacy Erasmus MC University Medical Centre Rotterdam P.O. Box 2040 3000 CA Rotterdam Tel: +31 10 4633202 Fax: +31 10 4636605 e-mail: r.vanhest@erasmusmc.nl _______________________________________________________ From: bachmanw@globomax.com Subject: RE: [NMusers] increasing ETA estimates with adding full block Date: 8/19/2003 8:36 AM Reiner, You really haven't given us enough information, but, based on what you have given us, some thoughts: 1. if you haven't used FOCE with interaction, do so. 2. if you have, I suggest that you just have a situation where a full omega is not warranted. as you have said yourself, the additional etas are NOT describing more of the variability (and the decrease in objective function is just due to adding more parameters to the model). 3. your variance models may not be appropriate for your data. William J. Bachman, Ph.D. GloboMax LLC 7250 Parkway Drive, Suite 430 Hanover, MD 21076 410-782-2212 bachmanw@globomax.com _______________________________________________________ From: Ken.Kowalski@pfizer.com Subject: RE: [NMusers] increasing ETA estimates with adding full block Date: 8/19/2003 10:01 AM Reinier, We really shouldn't think of estimating off-diagonal elements of omega as reducing variability in the diagonal elements of omega like we do when including fixed effects. It is hard to know with the limited information you provided whether a full block omega is warranted. How one partitions the variability in omega and sigma to descibe the total variability in the data may or may not be important based on the intended use of the model. My own approach to estimation of omega is to fit the fullest omega that can be supported by the data (i.e., avoiding over-parameterization or ill-conditioning) even if some of the off-diagnonal elements correspond to correlations near zero. In your case a 100 point drop in OFV would suggest that one or more off-diagonal elements correspond to correlations that are different from zero. If you plan to use your model for simulating individual responses those off-diagonal elements may be important. Ignoring those correlations by fitting a diagonal omega may result in unrealistic combinations of the individual parameters when you conduct simulations using the diagonal omega model fit. Regards, Ken _______________________________________________________