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  • [NMusers] Decrease in OFV with a fixed effect
    get an estimate of 10 4 for V1 variance may be because variance is truly small in which case adding covariate should not improve OFV or because it is downwards biased due to model misspecification e g through a diagonal omega structure In the latter case you can well expect an improvement in OFV if you add a true covariate and maybe if you add a false one too it is difficult to know what can happen in misspecified models Mats Mats Karlsson PhD Professor of Pharmacometrics Div of Pharmacokinetics and Drug Therapy Dept of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 SE 751 24 Uppsala Sweden phone 46 18 471 4105 fax 46 18 471 4003 mats karlsson farmbio uu se From Kowalski Ken Ken Kowalski pfizer com Subject RE NMusers Decrease in OFV with a fixed effect Date Mon August 23 2004 11 46 am Hi Pete To expand on Mats comments you should be careful not to over interpret a variance component that is estimated near zero to mean that there is no interindividual variability It may be that there is insufficient information in the design to accurately estimate this variance component I note that you indicate that for some subjects you only had 2 observations Also if there is model misspecification in omega by using a diagonal omega structure when there is a true non zero correlation between parameters say CL and V it may be that NONMEM will partition the interindividual variability into only one of these components I have observed that variance components estimated near zero say for V for a diagonal omega will no longer be estimated near zero when fitting a block omega that allows for the covariance to be estimated say between CL and V Based on these observations I have a couple of suggestions questions 1 If you fit a block omega structure does NONMEM still want to estimate the variance components for V1 Q2 and V2 near zero 2 For the model fit you describe below with a 21 point drop in OFV did you ee a reduction in the omega for CL relative to the estimate when you did not include the WT effect on V1 It may be that some of the interindividual variability is getting partitioned into the random effect for CL due to misspecification of omega If so then I would expect that the fixed effect for WT on V1 would to some extent reduce the omega estimate for CL Ken From Serge Guzy GUZY xoma com Subject RE NMusers Decrease in OFV with a fixed effect Date Mon August 23 2004 1 26 pm May be I did not understand but if you model V1 the way you did V1 across individuals will be different because of their difference in weight Your first model will give you the same V1 for everybody If I am right the two models are different If there is a real correlation between V1 and weight the

    Original URL path: http://nonmem.org/nonmem/nm/99aug232004.html (2016-04-25)
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  • [NMusers] NONMEM Bug VII
    Thu August 19 2004 11 08 am Hi I m in the process of completing a validation review cycle I had noted in the last round of validation that Bug VII in the official bug list for NONMEM Version V Level 1 1 File NONMEMbugs06APR2004 pdf on the Globomax site did not list a work around or a fix For this review I decided to see if anyone else has

    Original URL path: http://nonmem.org/nonmem/nm/99aug192004.html (2016-04-25)
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  • [NMusers] indirect response model
    each subject The question is how should I set up the data records and control stream to incorparate the baseline into my dataset I was able to fit the data without baseline data but the prediction for time 0 is all 0 Since in my case Rcort is not a parameter to estimate the F4 method does not work for me Thanks in advnace Kai Kai Wu Department of Pharmaceutics University of Florida Gainesville Fl Office phone 352 846 2730 From Nick Holford n holford auckland ac nz Subject RE NMusers indirect response model Date Wed August 18 2004 11 16 pm Kai Presumably if you could arrange for a constant rate input of something you could write a model to transform the constant input into a parametric circadian rhythm If you set up a large AMT e g 1000000 and a nominal RATE of 1 with CMT 4 then you can have a constant input to play with to make Rcort a rhythmic input for DADT 4 F4 would be used to set an appropriate scale for the input 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 Luann Phillips luann phillips cognigencorp com Subject RE NMusers indirect response model Date Thu August 19 2004 10 41 am Kai I would try adding your baseline measurements to the dataset starting at time 0 initialize the amount in compartment 4 to an appropriate value and include your equation for Rcort in PK if it is not dependent on continuous time TIME or in DES if dependent on continuous time T To initialize the amt in cmt

    Original URL path: http://nonmem.org/nonmem/nm/98aug182004.html (2016-04-25)
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  • [NMusers] Outliers and the FDA guideline
    From Robert L James rjames rhoworld com Subject RE NMusers Outliers and the FDA guideline Date Wed August 18 2004 8 33 am Thomas I always classify outliers as those that are 1 highly improbable and 2 those that are due to natural extremes in variation Highly improbable outliers strongly suggest experimental protocol error for example the lab technician left out an important reagent when performing the assay or a laboratory appartus wasn t properly zeroed or warmed up incomplete mixing of a drug in blood during the first minutes following an bolus arterial injection etc Highly improbable outliers are ususally near the limit of biologic impossibility Natural extremes outliers on the other hand are unlucky but real Biologic systems can occassionaly vary producing very extreme values For Highly improbable outliers I simply discard the outlier from all analyses and make a note of discarding it in my results However discarding natural extreme outliers are statistically problematic To discard them outright will bias the results by shrinking the variance Including them may make it very difficult to fit a good model For natural extreme outliers I initially exclude them from the data during the model fitting But then for my final model run I ll put natural extreme outliers back into my model so that the variance structure reflects the natural although extreme variability For this final run I may or may not fix the theta parameters to the estimates obtained by the earlier model without the outliers I report model diagnostics using the final model fit which was based on the data that included the natural extreme outliers Robert James From Hutmacher Matt Matt Hutmacher pfizer com Subject RE NMusers Outliers and the FDA guideline Date Wed August 18 2004 11 43 am Outliers are a difficult subject I think if you asked 10 different modelers you would get 10 different answers on how to handle them I would suggest a systematic approach to data elimination in general A systematic approach is the analyst s best surrogate for objectivity since only the reviewer audience can determine ultimately the level of objectivity For an analysis which will be submitted to a regulatory authority I would advocate specifying the criteria for classifying data as outliers a priori before unblinding the data in a population modeling analysis plan This document should also specify how the analyst will determine if the outlier is influential and how he she will proceed if the outlier is influential This systematic pre specified approach will mitigate the subjectivity induced by eliminating data a posteriori In general my opinion is that it is best to include all the data whenever possible If there are number of outliers one might try using a mixture of epsilons and hence variances to down weight these observations and reduce their influence Sometimes handling of outliers will depend on the goal of the analysis and the outliers may not fulfill pre specified criteria such as residuals 3 or 4 For example we did

    Original URL path: http://nonmem.org/nonmem/nm/99aug182004.html (2016-04-25)
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  • [NMusers] BUGLIST ERROR
    everyone In case anyone has been using the Official Bug List located at the NONMEM link on the Globomax home page http www globomax com products nonmem html to track updates you should be aware the files located there are not current There is a more up to date location at ftp ftp globomaxnm com Public nonmem buglist Details about the more up to date list can be found on

    Original URL path: http://nonmem.org/nonmem/nm/99aug172004.html (2016-04-25)
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  • [NMusers] NMPRD4 or LNP4 too small Error
    ERROR WAS FOUND ON LINE 245 AT THE APPROXIMATE POSITION NOTED Y F 1 ERR 1 ERR 2 X THE CHARACTERS IN ERROR ARE D00773 288 SIZE OF NMPRD4 EXCEEDED LNP4 IS TOO SMALL IN NM TRAN AND NONMEM Your assistance is very much appreciated From Sam Liao sliao pharmaxresearch com Subject RE NMusers NMPRD4 or LNP4 too small Error Date Mon August 16 2004 2 45 pm Your model

    Original URL path: http://nonmem.org/nonmem/nm/99aug162004.html (2016-04-25)
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  • [NMusers] Modeling of iv and po data
    alone a non linear CL gives the best fit Such a model however does not converge when iv and po data are combined The major problem I believe is the estimation of ka Any suggestion on how to proceed And in general if one of the characteristics of the model including iv data let s say the non linear CL is masked by the PK profile after po what would be the best approach to model the data Model the data separately or apply a simpler model to the combined data set best wishes Toufigh Gordi From Nick Holford n holford auckland ac nz Subject RE NMusers Modeling of iv and po data Date Wed August 11 2004 4 54 pm Toufigh If you have mixed order elimination detectable from the IV dose then it is quite possible that you will have rate dependent first pass extraction with the oral dose e g see Holford NHG Complex PK PD models an alcoholic experience International Journal of Clinical Pharmacology and Therapeutics 1997 35 10 465 468 I would not worry about lack of convergence until you have got a good handle on the structural model In the end the visual goodness

    Original URL path: http://nonmem.org/nonmem/nm/98aug112004.html (2016-04-25)
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  • [NMusers] COEFFICIENT MATRIX IS ALGORITHMICALLY SINGULAR
    removing their respective etas from the model Removing the zero ones in particular is often successful in getting a covariance step to run 3 change your model simpler is usually better if possible nmconsult globomaxnm com GloboMax LLC 7250 Parkway Drive Suite 430 Hanover MD 21076 Voice 410 782 2205 FAX 410 712 0737 From Kazimierz H Kozlowski khkoz czd waw pl Subject RE NMusers COEFFICIENT MATRIX IS ALGORITHMICALLY SINGULAR Date Wed August 11 2004 3 31 pm Dear Dr Nandy You probably need some simplificate of your codes i e one compartment model I suggest because it is zero order input and F1 is V1 F as sinkle parameter I m not sure but F1 factor not act for infusion and if you need estimate it could be S1 V1 F1 1000 but simultaneous oral and i v data are need It is good to avoid 0 values for clearaces and rate constant Your code I see as the above sincerely yours Kazimierz H Kozlowski Laboratory of Pharmacokinetics The Childrens Memorial Health Institute Warsaw Poland PROBLEM INPUT C TIME DV SUBJ ID AMT TYPE EVID MDV CMT RATE TAMT AGE HT WT SA SEX RCE DATA C IGNORE C SUBROUTINES ADVAN5 MODEL COMP CENTERAL DEFDOSE DEFOBS PK CLS THETA 1 EXP ETA 1 Induced Clearance CLI THETA 2 EXP ETA 2 Baseline Clearance KOUT THETA 3 EXP ETA 3 Turnover rate LAG THETA 4 EXP ETA 4 NEWT TIME LAG IF NEWT LE 0 NEWT 0 CL10 CLS CLS CLI EXP KOUT NEWT TVV1 THETA 5 V1 TVV1 EXP ETA 5 D1 THETA 6 Duration of zero order input F1 THETA 7 Bioavaialbility factor probably F1 is not active for infusion input please check K10 CL10 V1 S1 V1 1000 S1 V2 F1 1000 probably F1 is not active for infusion input please check or S1 V2 F1 1000 probably F1 is not active for infusion input please check ERROR FX 0 IF F EQ 0 FX 1 W F FX Y F 1 ERR 1 ERR 2 IPRE F IRES DV IPRE IWRE IRES W THETA 1 100 CLS clearance after induction 1 500 CLI clearance before induction base line clearance 1 300 KOUT induction constant 6 172 LAG lag time for induction of CL10 1 800 V1 F1 volume of distribution 1 10 D1 duration of zero order absorption 0 0 5 1 F1 bioavailability factor could be estimated with additional iv data OMEGA 5 5 5 4 0 5 SIGMA 0 2 5 ESTIMATION MAXEVAL 9999 PRINT 5 NOABORT METHOD 1 INTERACTION COV TABLE ONEHEADER NOPRINT FILE C tab ID TIME DV SUBJ AMT CL10 V1 ETA 1 ETA 2 ETA 3 IPRE K10 D1 F1 SCATTER PRED VS DV UNIT BY CMT SCATTER RES WRES VS PRED TIME ORD0 BY CMT SCAT PRED DV VS TIME BY CMT SCATTER IPRE VS DV UNIT BY CMT From Xiao Alan alan xiao merck com Subject RE NMusers COEFFICIENT MATRIX IS ALGORITHMICALLY SINGULAR Date Wed August 11 2004

    Original URL path: http://nonmem.org/nonmem/nm/99aug112004.html (2016-04-25)
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