Dear all, Thanks for your kind advice. I tried to dig out the solution based on your suggestion except for the demol definition, but unfortunately I can't identify any possible cause for the error message. There is no error in covariance step, estimated standard errors are within normal range, 20-40% as CV, and no unusual correlation was found in correlation matrix. An only certainty is that this error message is peculiar in NONMEM VI as Ken said. In fact, I haven't had this experience in NONMEM V, and the error message did not occur in even the former model.
Mitsuo > -----Original Message----- > From: BAE, KYUN-SEOP [mailto:[EMAIL PROTECTED] > Sent: Friday, November 14, 2008 5:09 AM > To: Ken Kowalski; Higashimori, Mitsuo; nmusers@globomaxnm.com > Subject: RE: [NMusers] NONMEM message > > > Hi, All, > > This is just brief my understanding. > > If common variable IRSET (a kind of reset indicator variable) > is set to > 1, this error message shows. > This variable is set with various conditions but without minimization > failure. > > The condition I found is that > > IRSETP==1 or IRSET > 0.1*ITN or (IRESET==1 and IER==0) > > Here ITN is iteration count and IER (presumably > integer/indicator/index > of) error return code. > > One reason (IRESET==1) is that 'RESET' event on Hessian matrix. > You may have seen the message like 'RESET HESSIAN, TYPE I' during the > minimization. > You may not see this message if you do not use PRINT=1 option. > > Anyway, this message means NONNEM experienced some difficulties during > minimization, but "recovered" without any significant error message. > So, user is requested to be careful and make decision with > "COVARIANCE" > together. > Because, "COVARIANCE STEP" is done in different subroutines from > "MIMINIZATION STEP" subroutines, successful and reasonable covariance > step supports that "MINIMIZATION STEP" was right. > > As others already mentioned, I don't care much about this message if > standard errors are reasonable. > > Thanks, > > ===================== > Kyun-Seop Bae MD PhD > > Email: [EMAIL PROTECTED], [EMAIL PROTECTED] > > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] > On Behalf Of Ken Kowalski > Sent: Thursday, November 13, 2008 07:36 > To: 'Higashimori, Mitsuo'; nmusers@globomaxnm.com > Subject: RE: [NMusers] NONMEM message > > Mitsuo, > > This is a new message specific to NONMEM VI. I must confess I don't > know what to make of this message myself. It would be informative if > someone could tell us what internals in NONMEM trigger this message > (i.e., "PROBLEMS OCCURRED WITH THE MINIMIZATION"). > > With respect to your two model runs note that they are really two > different parameterizations. In the first parameterization, where > > TVCL = THETA(1) * THETA(2) ** SEX > > note that THETA(1) represents the true value of CL for males and > THETA(2) represents the ratio of CL between females to males. In the > second parameterization, where > > TVCL = THETA(1) > IF (SEX.EQ.1) TVCL = THETA(2) > > note that THETA(1) and THETA(2) represent the true values of CL for > males and females, respectively. Thus, THETA(2) has a different > interpretation between these two parameterizations. > > A third parameterization that you could consider is > > TVCL = THETA(1) *(1 + THETA(2))**SEX or equivalently, TVCL = > THETA(1) * > (1 + > THETA(2)*SEX) > > where THETA(1) is again the true value of CL for males and THETA(2) is > the fractional change in CL for females relative to males. > > Each of these parameterizations should result in the same model fit > (i.e., minimum value of the OFV) but one parameterization may be more > stable than another...it is similar to the issue with continuous > covariates where we center or scale the covariate based on the mean or > median value (i.e., centering or scaling will reduce the > correlation in > the estimates between the intercept term and the covariate > effect which > should lead to a more stable model and faster convergence to > the minimum > OFV). > > I would look at the COV step output and in particular, look at the > correlation of the estimates between THETA(1) and THETA(2) for these > different parameterizations. My guess is that the > correlation is higher > for the first parameterization (given that you indicate that it gives > this warning message and that the second parameterization does not). > You can also use the PRINT=E option on the COV statement and > look at the > ratio of the largest to the smallest eigenvalues to more > globally assess > the stability of your model. In the end, if they all converge to the > same final OFV and if you really want to estimate the ratio of CLs > between males and females then so be it even if the model is > less stable > and NONMEM gives you this warning message. On the other hand, if the > different parameterizations don't converge to the same OFV > then you need > to look more closely at how you parameterize the covariate effect. If > you get a lower OFV with the second parameterization because > it is more > stable and NONMEM has an easier time iterating to the minimum > OFV then I > would go with that parameterization and if you want to estimate the > ratio of the CLs you can always estimate it as > THETA(2)/THETA(1) (i.e., which is equivalent to THETA(2) in the first > parameterization. > > I hope this helps. > > Ken > > Kenneth G. Kowalski > President & CEO > A2PG - Ann Arbor Pharmacometrics Group, Inc. > 110 E. Miller Ave., Garden Suite > Ann Arbor, MI 48104 > Work: 734-274-8255 > Cell: 248-207-5082 > Fax: 734-913-0230 > [EMAIL PROTECTED] > > > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Higashimori, Mitsuo Sent: Wednesday, November 12, 2008 10:06 PM To: nmusers@globomaxnm.com Subject: [NMusers] NONMEM message Dear all, I have a following error(?) massage on a poplation analysis using NONMEM VI. 0MINIMIZATION SUCCESSFUL HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. I found it when I described a control stream to assess sex difference on oral clearance as shown in below, TVCL = THETA(1) * THETA(2) ** SEX where SEX=0 for male and SEX=1 for female. This message was displayed without any error message. It was not dissapeared even though I changed the initial parameters. However, it was solved when I changed the model definition. For example, TVCL = THETA(1) IF (SEX.EQ.1) TVCL = THETA(2) Could you please let me know some details regarding the message. Especially, I'd like to know 1. What impact does this error message give the analysis result? 2. Why does it depend on the model definition? Thanks, _/ _/ _/ Mitsuo Higashimori, Ph.D. _/ _/ _/ Pharmacokinetic Group, Early Phase Development Department _/ _/ _/ Clinical Division, Research & Development _/ _/ _/ AstraZeneca K.K. _/ _/ _/ E-mail: [EMAIL PROTECTED]
