Hi Ethan,
I think you have given too little info to diagnose your problem properly. We don't even know if ETAs come in additively, proportionally, in logit expressions or what (so values of 2 or 3 doesn't give the scale). Also, I think that you mentioned 10-90% as values for correlations, whereas Bill interpreted it as CVs for IIV. It was just not enough info to make the distinction. If the model is so simple, why not show the whole model. Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Uppsala University Box 591 751 24 Uppsala Sweden phone: +46 18 4714105 fax: +46 18 471 4003 From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Bill Bachman Sent: Wednesday, April 15, 2009 7:46 PM To: 'Ethan Wu'; 'Bachman, William'; nmusers@globomaxnm.com Subject: RE: [NMusers] OMEGA selection In my opinion, I would not remove those in the 10-90% range. I would be suspect of anything over 100%, even with noisy data, they are being poorly estimated. _____ From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Ethan Wu Sent: Wednesday, April 15, 2009 1:15 PM To: Bachman, William; nmusers@globomaxnm.com Subject: Re: [NMusers] OMEGA selection some Etas estimated to be around 2 or 3, but since I am fitting a quite noisy PD data, I think they are actually reasonable no Etas close to 0 cov% esimated in the range of 10-90%.should those small ones like 10% be taken out? _____ From: "Bachman, William" <william.bach...@iconplc.com> To: Ethan Wu <ethan.w...@yahoo.com>; nmusers@globomaxnm.com Sent: Wednesday, April 15, 2009 12:12:56 PM Subject: RE: [NMusers] OMEGA selection Well, the first thing that I would do is look at the magnitude of the estimates of the etas. I would eliminate those etas that are poorly estimated (essentially the very large values or those approaching zero). _____ From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Ethan Wu Sent: Wednesday, April 15, 2009 11:47 AM To: nmusers@globomaxnm.com Subject: [NMusers] OMEGA selection Dear all, I am fitting a PD response, and the equation goes like this: total response = baseline+f(placebo response) +f(drug response) first, I tried full omega block, and model was able to converge, but $COV stop failed. To me, this indicates that too many parameters in the model. The structure model is rather simple one, so I think probably too many Etas. I wonder is there a good principle of Eta reduction that I could implement here. Any good reference? ICON plc made the following annotations. ---------------------------------------------------------------------------- -- This e-mail transmission may contain confidential or legally privileged information that is intended only for the individual or entity named in the e-mail address. If you are not the intended recipient, you are hereby notified that any disclosure, copying, distribution, or reliance upon the contents of this e-mail is strictly prohibited. If you have received this e-mail transmission in error, please reply to the sender, so that ICON plc can arrange for proper delivery, and then please delete the message. Thank You, ICON plc South County Business Park Leopardstown Dublin 18 Ireland Registered number: 145835
