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- [NMusers] Error when combining proportional residual error,

EXP EPS 4 EPS 8 ENDIF SIGMA BLOCK 1 1 SIGMA BLOCK 1 SAME SIGMA BLOCK 1 1 SIGMA BLOCK 1 SAME SIGMA 20 EPS 5 1000 EPS 6 1000 EPS 7 1000 EPS 8 From Serge Guzy GUZY xoma com Subject RE NMusers Error when combining proportional residual error Date Wed 2 Aug 2006 09 14 55 0700 I think the problem is that you did not define really a combined proportional with additive but instead an exponential error additive Can you try to write instead IF STDY EQ 1 THEN Y IPRD 1 EPS 1 EPS 5 ENDIF IF STDY EQ 2 THEN Y IPRD 1 EPS 2 EPS 6 ENDIF IF STDY EQ 3 THEN Y IPRD 1 EPS 3 EPS 7 ENDIF IF STDY EQ 4 THEN Y IPRD 1 EPS 4 EPS 8 ENDIF In the MCPEM program for example an exponential error variance does not really exist When you assume exponential error variance the data are log transformed and the error model is then assumed to follow a constant variance model In the MCPEM it does not make any sense to combine exponential with additive I guess something similar is used with NONMEM Serge Guzy President POP PHARM From Leonid Gibiansky leonidg metrumrg com Subject RE NMusers Error when combining proportional residual error Date Wed 02 Aug 2006 13 22 29 0400 No this is not the case with NONMEM It is perfectly OK to combine exponential and additive errors My guess is that STDY is not equal to 1 2 3 or 4 for some observation or there exist some other model coding error that is difficult to find without seeing the entire code To avoid STDY problem try Y IPRD EXP EPS 1 EPS 5 IF STDY EQ 2 THEN Y IPRD

Original URL path: http://nonmem.org/nonmem/nm/99aug022006.html (2016-04-25)

Open archived version from archive - [NMusers] Problems with an apparent compiler-senstive model

that NONMEM is good at diagnosing the quality of the fit is not known to me It seems to me that support for NONMEM doing a good job here is based on pretty slim evidence If you wish to make claims such as non convergence indicates problems with the model or with the data then I ask you to provide some concrete experimental evidence for this assertion Nick Nick Holford Dept Pharmacology Clinical Pharmacology University of Auckland 85 Park Rd Private Bag 92019 Auckland New Zealand email n holford auckland ac nz tel 64 9 373 7599x86730 fax 373 7556 http www health auckland ac nz pharmacology staff nholford From Mark Sale Next Level Solutions mark nextlevelsolns com Subject Re NMusers Problems with an apparent compiler senstive model Date Wed 02 Aug 2006 06 52 44 0700 For those interested I ve put a short paragraph and more importantly the Excel workbook that randomly sequenced the subjects with the results Web site is http www nextlevelsolns com downloads html click the link Excel spreadsheet macro examining effect of sequence of subjects on convergence near the bottom Mark Sale MD Next Level Solutions LLC www NextLevelSolns com From Leonid Gibiansky leonidg metrumrg com Subject Re NMusers Problems with an apparent compiler senstive model Date Wed 02 Aug 2006 10 24 01 0400 Nick The root of the non convergence is the instability of the model data combination For example there was PAGE poster by Lindbom et al http www page meeting org default asp abstract 997 that concluded The condition number of the covariance matrix of the original model is a strong predictor of NONMEM stability in the bootstrap and case deletion diagnostics You may choose to ignore this instability and get away with a reasonably good model but this is not the reason to dismiss a perfectly useful and very important diagnostic like the convergence status Note also that in your examples the authors started with the models that were studied to death to insure that these are the best possible models for the data in hands It was not clear whether the final models in those examples converged and discussion was centered only on the bootstrap samples Bootstrap samples by a nature of the problem too many of them cannot receive as much attention as the final model In any case it is premature to conclude from these examples that convergence is not important If you like confirmation of the statement non convergence indicates problems with the model or with the data try to estimate bioavailability CL and V at the same time in the absence of the reference formulation You may end up with the model that can even pass the most stringent scrutiny using the predictive check procedure but still is deficient and this deficiency is easily revealed by either non convergence or by the failure of the covariance step Leonid From Mark Sale Next Level Solutions mark nextlevelsolns com Subject Re NMusers Problems with an apparent compiler senstive model Date Wed 02 Aug 2006 08 52 44 0700 Lenoid I for one am not ready to discard convergence as a measure of model goodness I m not even prepared to discard covariance success as a measure of model goodness everything else being equal I will always prefer a model that converges does a covariance step over one that doesn t But at the same time I d suggest that covariance step or convergence isn t required to deem a model useful as we all know they are never correct or even final The hard choices are when a model that makes biological sense refuses to converge and simple empirial models do converge Next there are I beleive three factors that contribute to model instability meaning that the variance covariance matrix cannot be inverted and or the model fails the internal criteria for NONMEM to declare it converged These three factors overlap greatly and are very rarely black and white They are 1 Model dependent non identifiability your example you cannnot regardless of the amount quality of the data identify CL V and F with only oral data although I had an example where NONMEM converge successfully in such a case supporting Nicks position Essentially any value of F is consistent with the data with a corresponding value for CL and V In this case I beleive that the condition number rank of the covariance matrix would indicate this 2 Data dependent non identifiability Imagine that you want to estimate KA but all of your data is in the terminal phase Basically any value of KA is consistent with the data therefore the likelihood of the data isn t effected by the value of KA the objective function surface is flat in that dimension This will be true regardless of the quality of the data In this case as well I beleive that the condition number rank of the covariance matrix would indicate this Note the same root cause as data dependent non identifiability any value of a parameter is consistent with the data 3 Numerical problems Much more vague concept Partly related to quality of the data model misspecification residual error auto correlation But also includes true rounding errors which are most likely to be seen if we have a wide range of likelihoods between subjects e g some individuals have a lot of data some have little data But this source of rouding error is probably small compared to the model misspecification large residual error and autocorrelation Auto correlation BTW is known to be very very bad in linear regression resulting in bias in both parameter estimates and estiamtes of SE I m not aware that it has been studied much in non linear regression but I suspect it is a significant problem only partly addressed by the L2 variable Mark Sale MD Next Level Solutions LLC www NextLevelSolns com From Elassaiss Schaap J Jeroen jeroen elassaiss organon com Subject Re NMusers Problems with an

Original URL path: http://nonmem.org/nonmem/nm/99jul292006.html (2016-04-25)

Open archived version from archive - [NMusers] Xpose updated

anyway including completely revamped documentation preliminary Linux support numerous bugfixes and scatterplot matrices Downloads are available from the official Xpose website at SourceForge as usual and as always feedback is very welcome Full documentation of all available functions is now accessible from the website as well filling in for the complete users guide which we are preparing The website if anyone s forgotten is http xpose sourceforge net Justin Wilkins

Original URL path: http://nonmem.org/nonmem/nm/99jul242006.html (2016-04-25)

Open archived version from archive - [NMusers] Reporting of interim models in Pop PK Reports

base full and final models Lack of effect is to me a difficult question to address using Nonmem and therefore if the purpose of the evaluation was to show a lack of effect there would be considerable supportive evaluations to answer that question If I were only looking at covariates and trying to show that there was no apparent impact of a covariate on the PK eta plots would be as or more useful than a Nonmem run that shows no improvement in stated criteria Diane From Bill Bachman bachmanw comcast net Subject RE NMusers Reporting of interim models in Pop PK Reports Date Tue 18 Jul 2006 11 10 25 0400 Diane You are absolutely correct By lack of effect I meant something more like no apparent impact of a covariate on the PK Bill From David Dai david dai bms com Subject RE NMusers Reporting of interim models in Pop PK Reports Date Tue 18 Jul 2006 12 09 05 0400 I practice simlarly to Peter The main body of my last report is few hundred pages All important interim model runs are listed in separate tables The purpose is to show agency the logic line of model building and they can follow it better than only final model is provided Most models tested will then be listed in the appendix to support main report The big hassle of writing report like this is the appendix ended up with 1000 pages It also increases workload for QC scientist who has to check every number in the main report as well as appendix What s agency acceptance for a much condensed report any hint and tips david David Dai PhD Mail stop E14 07 Clinical Discovery Bristol Myers Squibb Company Lawrenceville NJ 08543 Phone 609 2526342 Fax 609 2527821 From Mark Sale Next Level Solutions mark nextlevelsolns com Subject RE NMusers Reporting of interim models in Pop PK Reports Date Tue 18 Jul 2006 11 13 19 0700 Sorry but I have to weigh in now Does the agency care about the model building logic Does anyone or do we only care if the final model passes some set of reasonableness criteria Wasn t it Thomas Edison who after several hundred failed attempts at a light bulb said No I ve found 200 ways not to make a light bulb IMHO even when exercised flawlessly the logic that we use to build select models is inherently flawed and in my experience I consistenly find major flaws in the logic of my own analyses let alone someone elses So if we are doing this to make cue scary music the agency happy should we first ask if they care actually I have but I ll let them speak for themselves But as pretty much everyone else I suspect I make a table of every relevant model with comments why it was accepted as current best or not and include control files for all relevant models in an appendix Except of

Original URL path: http://nonmem.org/nonmem/nm/99jul182006.html (2016-04-25)

Open archived version from archive - [NMusers] EMEA pop pk reporting

Level Solutions mark nextlevelsolns com Subject NMusers EMEA pop pk reporting Date Mon 17 Jul 2006 11 24 16 0700 For those of you who haven t seem this yet http www emea eu int pdfs human ewp 18599006en pdf

Original URL path: http://nonmem.org/nonmem/nm/99jul172006.html (2016-04-25)

Open archived version from archive - [NMusers] SS indication for dataset with BID dose and AM dose different from PM dose.

Looks like it would work J From Mark Sale Next Level Solutions mark nextlevelsolns com Subject RE NMusers SS indication for dataset with BID dose and AM dose different from PM dose Date Tue 11 Jul 2006 09 31 01 0700 Kim This is pretty easy which is the only reason I know the answer You give two doses with different doses as you have each with II 24 The only issue is that you have to use a value of 2 for the second SS dose Normally SS 1 it basically erases all previous doses and uses only and infinite series of doses backwards not forwards So the first dose probably has SS 1 erasing all previous doses and the second has SS 2 giving this dose on top of the previous dose But you will also have to put them in chronological order or is the date on the DV record of 1 1 2006 incorrect it is before the first dose ID DV AMT DATE TIME SS II 1 15 0 01 01 2006 08 01 1 0 0 8 01 01 2006 20 00 1 24 1 0 0 5 01 02 2006 08 00 2 24 SS labels PREDPP s steady state SS data item The steady state data item is optional It can take one of three values in any event record 0 indicates that the dose is not a steady state dose 1 indicates that the dose is a steady state dose and that the com partment amounts are to be reset to the steady state amounts resulting from the given dose Compartment amounts resulting from prior dose event records are zeroed out and infusions in progress or pending additional doses are cancelled The system is not totally reset the on off status

Original URL path: http://nonmem.org/nonmem/nm/99jul112006.html (2016-04-25)

Open archived version from archive - [NMusers] covariance step aborted.

error of estimate It showed covariance step aborted message could you please let me know how can I get standard error of estimate 2 I set upper bound as 8 and final estimate comes 8 In this case do I have to delete upper bound and set unlimited 3 With another data after 40 iteration NONMEM is still running but stuck with this What I have to do with this

Original URL path: http://nonmem.org/nonmem/nm/98jul102006.html (2016-04-25)

Open archived version from archive - [NMusers] Multiple dose analysis using NONMEM

the enzyme during drug exposure Various effect functions are possible e g iphosphamide Emax cyclophosphamide on off Anthe references Kerbusch T Huitema ADR Ouwerkerk J et al Evaluation of the autoinduction of ifosfamide metabolism by a population pharmacokinetic approach using NONMEM Br J Clin Pharmacol 49 555 561 2000 de Jonge ME Huitema ADR Rodenhuis S et al Integrated Population Pharmacokinetic Model of both cyclophosphamide and thiotepa suggesting a mutual drug drug interaction J Pharmacokinet Pharmacodyn 31 135 156 2004 Anthe Zandvliet Slotervaart Hospital Dept Pharmacy and Pharmacology Louwesweg 6 1066 EC AMSTERDAM The Netherlands Telephone 31 20 512 4657 FAX 31 20 512 4753 From Justin Wilkins justin wilkins farmbio uu se Subject Re NMusers Multiple dose analysis using NONMEM Date Mon 10 Jul 2006 13 28 48 0200 Dear Anthe Vipul The original question related to autoinhibition rather than autoinduction I guess but an enzyme turnover model could still be adapted to work in the way suggested One could also consider other semi mechanistic ways of dealing with the problem as well such as a competitive interaction model for example but I ve never seen one in practice There s another published example of an enzyme turnover model in Hassan M Svensson US Ljungman P Bjorkstrand B Olsson H Bielenstein M Abdel Rehim M Nilsson C Johansson M Karlsson MO A mechanism based pharmacokinetic enzyme model for cyclophosphamide autoinduction in breast cancer patients Br J Clin Pharmacol 1999 Nov 48 5 669 77 Here amount of drug in the central compartment increases the rate of production of enzyme in the enzyme compartment rather than its rate of elimination while the amount of enzyme in the enzyme compartment in turn increases clearance of the drug from the central compartment This seems to me to be more mechanistically appropriate than

Original URL path: http://nonmem.org/nonmem/nm/99jul102006.html (2016-04-25)

Open archived version from archive