RE: NONMEM vs SPSS

________________________________ From: [email protected] [[email protected]] on behalf of Elassaiss - Schaap, J (Jeroen) [[email protected]] Sent: Monday, March 31, 2014 12:23 PM To: Gavin Jarvis; [email protected] Subject: RE: [NMusers] NONMEM vs SPSS Dear Gavin, Reading back your original post, if your data are really N=1 and you have this perfect fit phenomenon there is probably little value in reporting the SEs. But on the other hand your new reply suggests that you are doing a simulation exercise… in which case a regression on aggregated data may be less productive. Perhaps you could consider doing an analysis with SSE (psn.sf.net, not sure whether WfN has similar tools) to figure out which design would support the models you are considering with little additional effort. Jeroen PS: bootstrap on N=1 does not work, the nonmem approaches use all sampling over subjects. (There are other ways of doing a bootstrap) From: [email protected] [mailto:[email protected]] On Behalf Of Gavin Jarvis Sent: Monday, March 31, 2014 11:33 To: [email protected] Subject: RE: [NMusers] NONMEM vs SPSS Dear All Thank you for all the very helpful comments. In reply: 1. MATRIX=R does make the standard error and correlation values much more similar to SPSS(NLR) 2. The residual error model is additive, homoscedastic (just ETA(1)). The data are extremely tight (R^2 >99.9%) – almost perfect! The purpose of my analysis is to assess structural models for analysing asymmetric dose-response curves. The problem is that some models produces parameters that lose empirical meaning and are very highly correlated. 3. I tried the bootstrap option using WFN. However, the parameter estimates all came out identical – probably because the data is so tight – this makes it tricky to evaluate standard errors! Gavin From: Bauer, Robert [mailto:[email protected]] Sent: 29 March 2014 20:46 To: Ken Kowalski; 'Gavin Jarvis'; [email protected]<mailto:[email protected]> Subject: RE: [NMusers] NONMEM vs SPSS I concur with Ken’s statement, and I also prefer to use MATRIX=R as the first choice for covariance assessment. On occasion, MATRIX=S can be used if there are numerical difficulties in assessing the R matrix, and if there are enough subjects relative to the dimension size (number of total parameters estimated) of the variance-covariance matrix to be estimated. Robert J. Bauer, Ph.D. Vice President, Pharmacometrics, R&D ICON Development Solutions 7740 Milestone Parkway Suite 150 Hanover, MD 21076 Tel: (215) 616-6428 Mob: (925) 286-0769 Email: [email protected]<mailto:[email protected]> Web: http://www.iconplc.com/ From: [email protected]<mailto:[email protected]> [mailto:[email protected]] On Behalf Of Ken Kowalski Sent: Saturday, March 29, 2014 3:44 PM To: 'Gavin Jarvis'; [email protected]<mailto:[email protected]> Subject: RE: [NMusers] NONMEM vs SPSS Dear Gavin, This is most likely because most nonlinear regression programs invert the Hessian (second derivative matrix of the model with respect to the parameters) to obtain the covariance matrix. This corresponds to the R matrix in NONMEM. However, the default method that NONMEM uses is a sandwich estimator involving both the Hessian (R) and the square of the first derivatives matrix (S). I suspect that if you use the MATRIX=R option on the $COV step you will find that the standard errors will now be in agreement with SPSS (NLR). I know Stu Beal made the sandwich estimator the default as it is supposed to be more robust to non-normality but I would have preferred the MATRIX=R option to be the default to be more consistent with other nonlinear regression software implementations. Ken From: [email protected]<mailto:[email protected]> [mailto:[email protected]] On Behalf Of Gavin Jarvis Sent: Saturday, March 29, 2014 12:55 PM To: [email protected]<mailto:[email protected]> Subject: [NMusers] NONMEM vs SPSS Dear NONMEM Users Does anyone have a view on the relative merits/reliability/accuracy of NONMEM ($COV step) vs SPSS (NLR) with respect to their derived values of the parameter standard errors and parameter correlation matrices? The data I am analysing are single subject (not population). Parameter estimates from the two programs are, to all intents and purposes, identical. However, the SE values from NONMEM $COV are consistently smaller by 1.5-2.0-fold. Any thoughts? Gavin __________________________________________________ Dr Gavin E Jarvis MA PhD VetMB MRCVS University Lecturer in Veterinary Anatomy Department of Physiology, Development & Neuroscience Physiological Laboratory Downing Street Cambridge CB2 3EG Tel: +44 (0) 1223 333745 Fellow and College Lecturer in Pharmacology Selwyn College Cambridge CB3 9DQ Tel: +44 (0) 1223 761303 Email: [email protected]<mailto:[email protected]> Web: http://www.pdn.cam.ac.uk/staff/jarvis Twit: @GavinEJarvis