Re: OMEGA-Block

Hauke Several comments: It is better to parametrized the model in terms of CL, V1, Q, V2. If you believe that VSS is the derived parameters (VSS=V1+V2), then VSS parameterization introduced additional correlation that you do not need. Usually, random effects on V2 and Q are not defined properly unless you have a very rich data. I would start with the model that has only random effects on CL and V1, then add random effect on V2 or Q (not both!), and only then try to put all effects together. In my experience, random effect on Q could be helpful for rich sampling, but it is unusual to see a dataset where you need both V2 and Q etas. Based on your results (absolute correlation of V1 and VSS) I would guess that random effect on V2 is not needed at all. One of the helpful diagnostics is to look on correlation of random effect for the problem where this correlation is not explicitly included. If the correlation is real, you will see it on the scatter plot matrix of random effect. Helpful diagnostic is the OMEGA value. If inclusion of correlations substantially increases variance estimates, you may have an over-parametrized model where extra correlation makes the model less stable. Very helpful diagnostic is to compare models (with and without correlations, or with and without extra random effect) by looking on IPRED vs IPRED and PRED vs PRED plots of models under investigation. If they show perfect correlation (coincide with the unit line) you may go with the simplest model (assuming other diagnostics do not tell you otherwise). Thanks Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Hauke Rühs wrote: > Dear NMusers, > > modelling a 2-compartmet model parameterized by CL, V1, VSS and Q, I got to a problem with which I don’t know how to deal with: After choosing my structural and statistic model (combined residual error model) I estimated the covariance matrix by including an OMEGA-BLOCK(4), which reduced the OFV by 15. The correlations between the parameters were all estimated to be minor (< 0.8). But when I model with a BLOCK(2) on VSS and V1, which I would expect to be positively correlated, the correlation is estimated to be -0.99. Additionally, the inclusion of BLOCK(2) does not significantly improve the OFV. > > So does it, after all, still make sense to include the BLOCK(2)? > > Generally, at which step of model-building would you recommend to test for parameter correlation? > > Thanking you in advance, > > Hauke > > ----------------------------- > Hauke Rühs > > Apotheker > Pharmazeutisches Institut > - Klinische Pharmazie - > An der Immenburg 4 > > 53121 Bonn > > Tel: + 49-(0)228 73-5781 > Fax: + 49-(0)228 73-9757 > > www.klinische-pharmazie.info