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  • [NMusers] NONMEM convergence criterion
    NMusers NONMEM convergence criterion Date Sun April 17 2005 8 09 am Colleagues Does anyone know how to alter the NONMEM convergence criterion to a measure of deviation from observations rather than number of significant digits in estimated parameters Thank

    Original URL path: http://nonmem.org/nonmem/nm/99apr172005.html (2016-04-25)
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  • [NMusers] Residuals and INTERACTION for simultaneous PK/PD, impossible?
    heteroscastic and shouldnt be used when residuals are expected to be homoscastic However I am confused as what to do in the simultaneous PK PD case Here my PK residuals are expected to be heteroscastic so INTEACTION should be used but my PD residuals are expected to be homoscastic so INTERACTION should not be used I cant do both at the same time but my data does contain both kinds of residuals at the same time There is much discussion in the archives about simultaneious PK PD but they seem to discuss finding the right parameters I could find no posts that brought up if INTERACTION should be used or not I seem to get better fitting without INTERACTION but I think this is simply just due to the fact that I have more PD data then PK data Anyone have any ideas about the correct way to approach this Thanks very much Doug Eleveld From Piotrovskij Vladimir PRDBE VPIOTROV PRDBE jnj com Subject RE NMusers Residuals and INTERACTION for simultaneous PK PD impos sible Date Fri April 15 2005 3 18 pm Doug The only solution to your problem that I see is to make the residual error for

    Original URL path: http://nonmem.org/nonmem/nm/98apr152005.html (2016-04-25)
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  • [NMusers] simulation of dose escalation study
    SIGMA 0 01 SIMULATION 1 ONLYSIM TABLE SID ID TIME AMT DV DV NUL FILE 006 TAB NOAPPEND NOPRINT ONEHEADER NOFORWARD TABLE CID EMAX LEV FILE DATA CSV NOAPPEND NOPRINT NOHEADER NOFORWARD 007 ctl PROB RUN 007 INPUT SID ID TIME AMT DVR DV NUL DATA 006 TAB IGNORE SUBROUTINES OTHER NMUSERS FOR PRED FIRST REAL CURID ICOV1 ICOV2 MID LOGICAL READ IF NOT READ THEN CALL READER CURID 1 READ TRUE ENDIF IF NEWIND LT 2 THEN CALL GET SUB NEWIND ID CURID ICOV1 ICOV2 MID ENDIF COV1 ICOV1 COV2 ICOV2 EMAX ICOV1 LEV ICOV2 IF LEV EQ 50 NEXT 100 IF LEV EQ 100 NEXT 200 IF LEV EQ 200 NEXT 400 IF LEV EQ 400 NEXT 500 IF LEV EQ 500 NEXT 600 IF LEV EQ 600 NEXT 700 IF LEV EQ 700 NEXT 800 IF LEV EQ 800 NEXT 1000 IF LEV GE 1000 NEXT LEV 100 IF EMAX GT 50 THEN LEVN LEV ELSE LEVN NEXT ENDIF SIDN SID 1 X THETA 1 EXP ETA 1 Y X THETA 1 OMEGA 0 0001 SIMULATION 9999 ONLYSIM TABLE SIDN ID TIME AMT LEVN NUL NUL FILE 006 TAB NOPRINT ONEHEADER NOFORWARD TABLE SID ID TIME LEV EMAX DVR FILE 007 TAB NOPRINT ONEHEADER FORWARD 006 2 ctl Model Desc base model Project Name pkpd simulaties Project ID NO PROJECT DESCRIPTION PROB RUN 006 INPUT SID ID TIME AMT LEV DV NUL DATA 006 TAB IGNORE SUBROUTINES ADVAN6 TOL 5 MODEL COMP PK DEFDOS COMP PD DEFOBS COMP EMAX PK F1 LEV S1 THETA 1 EXP ETA 1 K12 THETA 2 EXP ETA 2 S2 THETA 3 EXP ETA 3 K20 THETA 4 EXP ETA 4 IF TIME EQ 0 CID 1 IF TIME EQ 1 CID 2 IF TIME EQ 2 CID 3 IF TIME EQ 3 CID 4 IF TIME EQ 4 CID 5 IF TIME EQ 5 CID 6 IF TIME EQ 6 CID 7 IF TIME EQ 7 CID 8 IF TIME EQ 8 CID 9 IF TIME EQ 9 CID 10 IF TIME EQ 10 CID 11 IF TIME EQ 11 CID 12 IF TIME EQ 12 CID 13 DES DADT 1 K12 A 1 DADT 2 K12 A 1 K20 A 2 IF DADT 2 GT 0 THEN DADT 3 DADT 2 ELSE DADT 3 0 ENDIF ERROR IPRED F Y IPRED EXP EPS 1 E A 2 S2 EMAX A 3 S2 THETA 1 S1 0 5 K12 1 S2 0 3 K20 OMEGA 0 01 0 01 0 01 0 01 SIGMA 0 01 SIMULATION 2 ONLYSIM TABLE SID ID TIME AMT DV DV NUL FILE 006 TAB NOAPPEND NOPRINT ONEHEADER NOFORWARD TABLE CID EMAX LEV FILE DATA CSV NOAPPEND NOPRINT NOHEADER NOFORWARD Slotervaart Hospitalÿ Dept Pharmacy and Pharmacology Louwesweg 6 1066 EC AMSTERDAM The Netherlands Telephone 31 20 512 4657 FAX 31 20 512 4753 From Tsai Max max tsai spcorp com Subject RE NMusers simulation of dose escalation study Date Fri April 15 2005 8 07 am

    Original URL path: http://nonmem.org/nonmem/nm/99apr152005.html (2016-04-25)
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  • [NMusers] population size and confidence power
    investigate how results depend on your assumptions Simulations may include extra layer or uncertainty about population parameters rather than select values for simulations you may assume their distributions As a rough estimate 6 is definitely too small 1000 should be sufficient I would say 200 300 should be sufficient unless you have a high variability of PK and or PK PD parameters or strong non linearity Leonid From Nick Holford n holford auckland ac nz Subject Re NMusers population size and confidence power Date Mon April 4 2005 3 48 pm NONMEM note spelling is not designed to directly compute power However it is possible to use NONMEM via simulation to estimate the power of a design to test a particular hypothesis IMHO any a priori power prediction requires the user to specify 1 The model parameters CL V Emax EC50 etc and the effect size of interest e g 30 difference in CL in a sub population or Emax with some particular value 2 The random effect size e g 50 apparent CV in CL and V etc plus 10 residual error 3 The hypothesis testing procedure e g likelihood ratio test 4 A design e g 20 subjects with samples taken at 6 specified times 5 A model e g one compartment disposition with bolus input and immediate drug effect described by an Emax model Once you have thought about the problem and you can specify all these features you are in a position to explore the power of the design by varying the number of subjects in the design to see how power varies You can use NONMEM to simulate a large number of studies with a particular design and then test the hypothesis for each simulated study If 80 out of 100 such studies fail to reject the null hypothesis then you could conclude that the power of the design is about 80 Your question is a bit ambiguous and perhaps you have something else in mind e g you want to estimate a confidence interval for a parameter of the model The most robust method for doing this with NONMEM is to use a bootstrap approach see http wfn sourceforge net wfnbs htm for some background on how this might be done Or perhaps you are interested in deciding which model is most suitable for making predictions of response The posterior predictive check and similar procedures that use the model to simulate predicted values may be helpful Yano et al 2001 Nick Yano Y Beal SL Sheiner LB Evaluating pharmacokinetic pharmacodynamic models using the posterior predictive check J Pharmacokinet Pharmacodyn 2001 28 2 171 92 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 Nick Holford n holford auckland ac nz Subject Re NMusers population size and confidence power Date Mon April

    Original URL path: http://nonmem.org/nonmem/nm/99apr042005.html (2016-04-25)
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  • [NMusers] San Francisco Bay Area PK/PD network
    a large portion consisting of small biotech companies most PK PD scientists might be the only source of these skills in their company with lots of assignments leaving very little time for keeping up with general advancement in the field or occasional discussions with other PK PD scientists I am aware of other scientific fields having a loose network arranging for occasional meetings or lectures in the Bay Area To my knowledge no such network exists for PK PD scientists in the local industry although we have very strong academic sites in this geographical area I d like to hear from those of you working in the Bay Area whether there is a need for such a network and an interest in helping to build it I invite those interested in this to contact me directly preferably through e mail for preliminary discussions Best regards Toufigh Gordi PhD Associate Director of Clinical Pharmacology CV Therapeutics Inc 3172 Porter Dr Palo Alto CA 94304 Tel 650 384 8929 From Steven Shafer steven shafer Stanford Edu Subject RE NMusers San Francisco Bay Area PK PD network Date Sat April 2 2005 10 49 am Dear Dr Gordi Excellent idea I would be

    Original URL path: http://nonmem.org/nonmem/nm/98apr012005.html (2016-04-25)
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  • [NMusers] Difference between typical values and geometric mean of posthoc values
    5 S1 V1 ERROR Y F EXP ERR 1 Starting at the exact values THETA 0 3 64 0 3 01 0 6 44 0 0 51 0 0 048 0 0 051 OMEGA 0 0533 0 1217 0 1063 0 1245 0 2215 0 0650 SIGMA 0 01 SIGMA 0 04 Some 12 37 44 49 54 71 84 92 used this to get convergence ESTIMATION MAX 9999 SIG 6 METHOD COND NOABORT POSTHOC TABLE TIME V1 V2 V3 CL Q2 Q3 DV SCATTER PRED VS DV UNIT From Eleveld DJ d j eleveld anest umcg nl Subject RE NMusers Difference between typical values and geometric mean of posthoc values Date Fri April 1 2005 9 14 am Thanks to everyone who replied to my question about the difference between typical values and geometric mean of posthoc values I am afraid I am still a bit lost In monte carlo tests I am seeing a bias of 10 between the typical values and geometric mean of posthoc values for CL Other parameters have lower biases This is for 100 estimations of 10 individuals each I am using ADVAN11 TRANS4 and the FOCE method Interestingly the geometric mean of posthoc values is closer to the real values than the typical THETA values I have no idea why this is I believe that the maximum likelihood estimate of a log normal distribution is the geometric mean So if the typical values THETA are the maximum likelihood values then there should be no bias with the real values I dont think using INTERACTION should be used beacuse all invididuals have the same error variance I dont think there are at least there shouldn t be model complexity problems The data is simulated and there is no model misspecification So I am still a bit lost Why should we rely on THETA values to describe central tendency of our parameter distributions when the POSTHOC values are available and seem to allow better accuracy Thank you Doug Eleveld From Wang Yaning WangYA cder fda gov Subject RE NMusers Difference between typical values and geometricmea n of posthoc values Date Fri April 1 2005 1 08 pm Eleveld Try a simple linear mixed effect model in NONMEM like Yij Intercepti slopei tij to see whether you still have this kind of observation Here are some of my opinions about nonlinear mixed effect modeling regarding your questions 1 Nonlinear mixed effect modeling parametric is not a pure maximum likelihood estimation method The likelihood is approximated in NONMEM also in other softwares Furthermore there are different methods for approximation The impact of these approximation on the consistency of the parameter estimates is not clear yet as far as I know 2 Log normal distribution is also approximated as a result of likelihood approximation or in order to approximate the likelihood depending on whether you start with linearization of the random effect or Laplacian approximation of the likelihood Therefore even though you simulated log normal samples they are

    Original URL path: http://nonmem.org/nonmem/nm/99mar302005.html (2016-04-25)
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  • [NMusers] dose compartment
    user guide part V chapter 6 section 8 2 1 page 58 it says an IM injection can be considered as dose to peripheral compartment I am wondering if one wishes to use a mono exponential decline model like ADVAN2 to fit IM data like this than by default there is no peripheral compartment My question specifically is what should be the appropriate compartment for assigning IM dose depot or peripheral I have another issue may be because of delayed sampling time points I can not see a absorption phase in the IM data and it looks very much similar to IV data with the first sampling point being Cmax further the bioavailability 100 In such a case what should be the ideal way to model this data 1 Fix KA based on literature reports or 2 To ignore absorption and model this data just like IV dosing with dose input directly to central compartment I do not know if this is an appropriate example but in the NONMEM user guide part V chapter 2 section 3 2 page 13 for a similar situation like this where time points after oral dosing were delayed and absorption was assumed to be

    Original URL path: http://nonmem.org/nonmem/nm/98mar302005.html (2016-04-25)
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  • [NMusers] Window of absorption
    switching from the first absorption rate constant Ka1 occurs earlier in profile and completed within 30 minutes to the second absorption rate constant Ka2 larger of two constants which may represent the optimal absorption site Regards Majid Vakily Ph D Senior Research Investigator Department of Drug Metabolism Pharmacokinetics Phone 847 582 2198 Fax 847 582 2388 From majid vakily tap com Subject Re NMusers Window of absorption Date Mon March 28 2005 4 10 pm Hi Sorry the drug follows 2 compartment model Regards Majid Vakily Ph D Senior Research Investigator Department of Drug Metabolism Pharmacokinetics Phone 847 582 2198 Fax 847 582 2388 From Gordi Toufigh Toufigh Gordi cvt com Subject RE NMusers Window of absorption Date Tue March 29 2005 1 20 pm Dear Majid Two solutions come in my mind Before describing them let me just warn you that I have no experience with such a system and my proposals specially the second one might not work Here they come Construct an absorption system with at least 2 compartments similar to a transduction PD model where you have the drug administered into the first compartment Initial condition for the second compartment is equal to 0 The compound leaves the first compartment via 2 routs absorption to the central compartment and transfer to the second absorption compartment You may want to test whether a single KA will work for both compartments or if you need different ones for each absorption compartment Write an IF THEN statement where you have a KA until a certain time and another KA after that As I said I would start with the first option as it is more close to what happens in reality you have a transfer of your compound through the GI track with different segments having different absorption capacities

    Original URL path: http://nonmem.org/nonmem/nm/99mar282005.html (2016-04-25)
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