Due To Rounding Errors Error=134
I used log transformed data. But I kept getting Rounding Error message: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 0MINIMIZATION TERMINATED DUE TO ROUNDING ERRORS (ERROR=134) NO. OF FUNCTION EVALUATIONS USED: 958 NO. OF SIG. DIGITS UNREPORTABLE ETABAR IS THE ARITHMETIC MEAN OF THE ETA-ESTIMATES, AND THE P-VALUE IS GIVEN FOR THE NULL HYPOTHESIS THAT THE TRUE MEAN IS 0. ETABAR: 0.13E-01 -0.62E-02 0.66E-02 -0.39E-02 0.49E-01 0.44E-01 P VAL.: 0.86E+00 0.93E+00 0.94E+00 0.96E+00 0.74E+00 0.67E+00 1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Could someone please shed some light on what is causing this error and what I should do to get rid of it? The model fit at the point of termination looks quite good, and the parameter estimates are quite good too. Thank you. Jeri Sottos ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ $PROBLEM 2Comp Parent Drug 1Comp Metabolite Model -- Log Transformed DV $DATA DATA_LOG.PRN $INPUT ID AMT RATE TIME DV CMT EVID $SUBROUTINES ADVAN6 TRANS1 TOL=5 $MODEL NPAR=6 NCOMP=3 COMP=(PARCEN DEFDOSE DEFOBS) ;CENTRAL COMPARTMENT FOR PARENT DRUG COMP=(PARPER NODOSE) ;PERIPHERAL COMPARTMENT FOR PARENT DRUG COMP=(METCEN NODOSE) ;CENTRAL COMPARTMENT FOR METABOLITE $PK CLt=THETA(1)*EXP(ETA(1)) ;PARENT DRUG CENTRAL CLEARANCE Vc=THETA(2)*EXP(ETA(2)) ;PARENT DRUG CENTRAL COMPARTMENT VOLUME CLd=THETA(3)*EXP(ETA(3)) ;PARENT DRUG DISTRIBUTION CLEARANCE Vp=THETA(4)*EXP(ETA(4)) ;PARENT DRUG PERIPHERAL COMPARTMENT VOLUME CLtm=THETA(5)*EXP(ETA(5)) ;METABOLITE CENTRAL CLEARANCE Vcm=THETA(6)*EXP(ETA(6)) ;METABOLITE CENTRAL COMPARTMENT VOLUME S1=Vc/1000 S3=Vcm/1000 $DES DADT(1)=-(CLt+CLd)/Vc*A(1)+CLd/Vp*A(2) DADT(2)=CLd/Vc*A(1)-CLd/Vp*A(2) DADT(3)=-CLtm/Vcm*A(3)+.4045*CLt/Vc*A(1) ;40.45% OF PARENT DRUG IS COVERTED INTO METABOLITE $ERROR FLAG=0 IF(AMT.NE.0)FLAG=1 ;dosing records only IPRED=LOG(F+FLAG) ;transform the prediction to the log of the ;prediction ; IPRED=log(f) for concentration records and ; IPRED=log(f+1) for dose records R1=0 IF (CMT.EQ.1) R1=1 R2=0 IF (CMT.EQ.3) R2=1 Y1=IPRED+EPS(1) Y3=IPRED+EPS(2) Y=R1*Y1+R2*Y3 YORI=EXP(Y) ; y in normal scale DVORI=EXP(DV) ; DV in normal scale IRES=EXP(DV)-EXP(IPRED) $THETA (10,33) (10,27) (30,60) (30,86) (10,17) (10,36) $OMEGA BLOCK(6) .24 .01 .28 .27 .01 .36 .20 .16 .25 .32 .24 .05 .24 .10 0.77 .17 .03 .09 .02 0.43 0.47 $SIGMA BLOCK (2) .06 .09 .18 $ESTIMATION ME
only measure the parent compound concentration which was far below the dose. For example, 120 umol was given as i.v infusion for 1 h but the plasma concentration of the parent compound is only around 0.02-1.5 uM. I just tried the simple ADVAN3 TRANS4 as follows, however, the result turns out to be a very large V2 and very small CL and keeping giving the rounding error ( error=134)! Does anyone can help me figure it out? $PK V1=THETA(1)*EXP(ETA(1)) V2=THETA(2)*EXP(ETA(2)) CL=THETA(3)*EXP(ETA(3)) Q=THETA(4)*EXP(ETA(4)) K=CL/V1 K12=Q/V1 K21=Q/V2 S1=V1 $ERROR IPRED=F W=F IRES=DV-IPRED IWRES=IRES/W Y= IPRED+W*EPS(1) $OMEGA 0.25 $OMEGA 0.5 $OMEGA 0.5 http://www.cognigencorp.com/nonmem/nm/98may132003.html $OMEGA 0.5 $SIGMA 0.25 Thanks a lot. Ping _______________________________________________________ From: Nick Holford n.holford@auckland.ac.nz Subject: Re: [NMusers] Help on rounding error ( error=134) ! Date: Mon, 13 Nov 2006 11:50:39 +1300 Ping, Did you look at the model predictions? If the predictions look Ok then you can ignore the rounding error message. A visual predictive check would be a good way to decide if your model predictions are OK. You http://www.cognigencorp.com/nonmem/nm/99nov122006.html say CL is small and V2 is large. On what basis of prior knowledge do you judge them to be small and large? Unless you have some good reason e.g. CL much bigger than cardiac output then it isn't usually helpful to judge the numerical values to be small or large. 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: "Toufigh Gordi" tgordi@Depomedinc.com Subject: Re: [NMusers] Help on rounding error ( error=134) ! Date: Sun, 12 Nov 2006 20:19:14 -0800 Hi Ping, I am guessing that you have looked at some graphs of your data and can clearly see a bi-phasic decline, or have some prior knowledge about the drug, and that's why you apply a 2-comp model. I would suggest that early on in the model-building, you start with one ETA (my suggestion would be CL or VC). If you find a model that does a relatively good job, you can start adding ETAs to see whether they improve the fit. I don't knwo about the NONMEM codes for various errors have have obviously not seen your data but your problem might be due to over-parametrization. Toufigh _______________________________________________________
is quite unreliable when it comes to helping me to decide if it has converged in a consistent and meaningful way." https://www.mail-archive.com/nmusers@globomaxnm.com/msg00453.html NONMEM is consistent in giving the same unhelpful information about whether it believes it can claim convergence i.e. with exactly the same compiler+options, CPU, NONMEM patch level it gives the https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797537/ same numbers (change any of these and of course you are quite likely to get something different). But it is inconsistent in the real world sense of giving me a due to solid feeling that it really converged in a way that gives me some confidence in the results. I have cited investigations that show one cannot have this kind of confidence because the parameter estimate distribution is equivalent whether or not NONMEM claims to have not converged (e.g. due to rounding errors), converged but no $COV or converged with $COV. So I due to rounding ask for SIGDIG=6 and ignore NONMEM's reported convergence status. I typically get more than 3 sig digs on the runs that interest me and often more than 6 and once in a while $COV is successful. But I use other criteria to judge suitability of the model - esp simulation based checks because these are in the spirit of what I really want to use the model for. Standard errors are just part of a historical description of a model with no practical relevance to predictive checks. Real world application of modelling implicitly or explicitly requires a prediction from the model. Yes -- Like you I like to see the run converge and %COV complete but I also like to see the sun shine every day. If it doesnt shine my life goes on... (its raining today in Auckland). NONMEMs termination messages are as reliable as the weather in this part of the world. Nick Mark Sale - Next Level Solutions wrote: > > Nick, > I'm interested in exactly what you mean by "unreliable". Is it > sensit
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help Journal ListHAL Author ManuscriptsPMC2797537 J Pharmacokinet Pharmacodyn. Author manuscript; available in PMC 2009 Dec 30.Published in final edited form as:J Pharmacokinet Pharmacodyn. 2008 Dec; 35(6): 661–681. Published online 2009 Jan 7. doi: 10.1007/s10928-008-9105-5PMCID: PMC2797537HALMS: HALMS383725Inserm subrepositoryDrug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 2: clinical trial resultsMarylore Chenel,1,* François Bouzom,2 Fanny Cazade,1 Kayode Ogungbenro,3,4 Leon Aarons,3,4 and France Mentré51IRIS, Institut de Recherches Internationales Servier Laboratoire Servier, 9 place des Pléiades 92415 Courbevoie Cedex,FR2Technologie Servier SERVIER, 27 rue Eugene Vignat, BP 11749, 45007 Orlé;ans,FR3CAPR, Centre for Applied Pharmacokinetic Research University of Manchester, School of Pharmacy and Pharmaceutical Sciences,GB4School of Pharmacy and Pharmaceutical Sciences University of Manchester, Oxford road, Manchester, M13 9PL,GB5Modèles et méthodes de l'évaluation thérapeutique des maladies chroniques INSERM : U738, Université Denis Diderot - Paris VII, Faculté de médecine Paris 7 16, Rue Henri Huchard 75018 Paris,FR* Correspondence should be adressed to: Marylore Chenel ; Email: moc.srgten.rf@lenehc.erolyramAuthor information ► Copyright and License information ►Copyright notice and DisclaimerThe publisher's final edited version of this article is available at J Pharmacokinet PharmacodynSee other articles in PMC that cite the published article.AbstractPurposeTo compare results of population PK analyses obtained with a full empirical design (FD) and an optimal sparse design (MD) in a Drug-Drug Interaction (DDI) study aiming to evaluate the potential CYP3A4 inhibitory effect of a drug in development, SX, on a reference substrate, midazolam (MDZ). Secondary aim was to evaluate the intera