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  • [NMusers] $ERROR Block: Target mediated disposition
    A 3 KD 2 4 KD A 3 DADT 1 KINT A 1 CL VC KPT KINT CF VC KTP A 2 DADT 2 KPT CF VC KTP A 2 DADT 3 KSYN KINT KDEG A 1 CF VC KDEG A 3 CF Free Drug measured A 1 Total Drug not measured A 2 Tissue Compt not measured A 3 Target measured Initializing DADT 3 with AMT 1 in the dataset Although I have written a differential equation for the total drug my observation is the free drug CF and I am not quite sure how do I or can I express that in the ERROR block since the CMT argument only relates to the corresponding differential equation according to my limited knowledge about NONMEM Any suggestion or help would be greatly appreciated Thank you Regards Ana From Samtani Mahesh PRDUS MSamtani prdus jnj com Subject RE NMusers ERROR Block Target mediated disposition Date Sun 9 Apr 2006 15 40 47 0400 Hello Ana Here are a few humble suggestions on how to implement TMDD in NONMEM 1 These models are notoriously stiff in nature so I would recommend that you start simple Unless you have evidence otherwise Non compartmental diagnostics suggested by Mager Jusko or in vitro data or your target data suggesting dramatic changes I would recommend that you start with a model where you don t have receptor turnover and no internalization A code for such a simple model is provided below 2 When you have multiple DVs in your dataset receptor and free drug you need If statements under ERROR and a flag in your dataset Please see this posting that nicely describe the modeling trick http www cognigencorp com nonmem nm 97aug062004 html 3 You may want to try ADVAN9 for these models There is

    Original URL path: http://nonmem.org/nonmem/nm/99apr082006.html (2016-04-25)
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  • [NMusers] NONMEM graphics
    a general purpose Excel macro for making plots from NONMEM output This can be downloaded from the Next Level Solutions web site www NextLevelSolns com downloads html To install unzip the file put in the c drive and use the Use folder Names option in Winzip this will put the files in the directory c Program Files NMMacros Any feedback is appreciated Feel free to try the parallel bootstrap application

    Original URL path: http://nonmem.org/nonmem/nm/99apr072006.html (2016-04-25)
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  • [NMusers] What are the best studies to combine?
    error is different So yes by all means combine data if the objectives of the analysis warrant it But consider all the possible biologically plausible ways that the different populations might be different and test the ones you can test Not sure what you mean by estimate of KA given observation prior to dosing is this an endogenous compound But in general yes it should be OK Mark Sale MD Next Level Solutions LLC www NextLevelSolns com From Abutarif Malaz malaz abutarif spcorp com Subject RE NMusers What are the best studies to combine Date Thu 30 Mar 2006 15 56 48 0500 This is my take and I hope we ll get comments from others To test your question I would look at the following 1 Residual plots separating or different colors for the data points from the different studies i e one color for data points in study 1 another for study 2 etc This way you ll get a rough idea of any inter study differences or if one study is on one side of the plot while the other is on the other side 2 If you feel you have enough data you may add the study number as a covariate 3 Look at time point distribution time post last dose from the different studies and try to see which study may skew the estimates of certain PK parameters e g ka Test for the importance of these parameters using sensitivity analysis again the usefulness of this may depend on the type of data you have And before all that look at the clinical relevance See if it makes sense to have a different Ka for patients and Healthy volunteers if the exposures at comparable time points and doses are similar between studies or in the same ballpark if there is a reason to believe that half life may be different etc And the most important question in my mind is Why are you combining the data for what purpose i e are you combining the data to have a bigger data set for covariate analysis or are you combining the data to have better confidence in the PK parameters for the patient study to simulate estimate individual exposures for patients in that study Depending on what the purpose of the modeling exercise is it may or may not matter how good bad the outcome of the points above is This is how I personally think about the analysis when combining several data sets I hope to hear from others how they approach this issue Malaz From musor000 optonline net Subject Re RE NMusers What are the best studies to combine Date Thu 30 Mar 2006 18 24 59 0500 Hello Malaz and Mark Thank you for your responses The primary objective is to look how demographic covariates affect PK parameters The problem question is Will such diverse studies provide reliable information on covariates gender weight and age If SC study of diseased subjects provide reliable

    Original URL path: http://nonmem.org/nonmem/nm/99mar302006.html (2016-04-25)
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  • [NMusers] GAM analysis and further action
    highly correlated covariates you can only include one of them in your GAM analysis most likely BW GAM analysis in X pose does not account for correlated covariates since univariate analyses are performed Some useful references discussing it 1 Anderson BJ McKee D Holford NHG Size myths and the clinical pharmacokinetics of analgesia in paediatric patients Clinical Pharmacokinetics 1997 33 313 327 2 Anderson BJ Woolard G Holford NHG A model for size and age changes in the pharmacokinetics of paracetamol in neonates infants and children Br J Clin Pharmacol 2000 50 125 134 3 Holford NHG A size standard for pharmacokinetics Clinical Pharmacokinetics 1996 30 329 332 I hope this was helpful Manish Manish Gupta PhD Post Doctoral fellow Clinical Pharmacology Therapeutics The Children s Hospital of Philadelphia Manish Gupta PhD Post Doctoral fellow Clinical Pharmacology Therapeutics The Children s Hospital of Philadelphia From Mark Sale Next Level Solutions mark nextlevelsolns com Subject RE NMusers GAM analysis and further action Date Wed 29 Mar 2006 12 16 37 0700 Mats Toufigh I have in general been unimpressed with both the sensitivity and specificiy of plots of post hoc etas vs potential covariates So I would add your first test plots to Mats list of things that are at best a guide on what to try I will once again suggest that the complexities of the interdependencies of covariate relationships and interdependencies of structural effects are a violation of the assumptions on which the post hoc vs covariate plot modeling approach and the step wise modeling approach is based As such we need to reasses the basic tenets of how we select models I m not sure about what assumptions underlie that GAM approach Still looking for collaborators for a formal assessment of traditional GAM WAM GA model selection strategy anyone interested Mark Sale MD Next Level Solutions LLC www NextLevelSolns com From Dennis Fisher fisher plessthan com Subject NMusers GAM analysis and further action Date Wed 29 Mar 2006 11 47 40 0800 Manish suggested a allometric model Although this approach MIGHT be appropriate from a PHYSIOLOGIC perspective I truly doubt that it is helpful from a CLINICAL perspective If we report that CL varies as a function of weight 0 75 what clinician will be able to use this information to guide dosing So I prefer to define models in terms that can be useful in the clinical setting Dennis Fisher MD P www PLessThan com From Charnick Steven B steven charnick merck com Subject RE NMusers GAM analysis and further action Date Wed 29 Mar 2006 14 56 15 0500Mark I would be very interested in the collaboration I ve been working on variations of this approach for some years now and while there appears to some meeting of the minds as to what is preferred I ve seen no formal assessment I agree that it s needed Steven Charnick PhD Senior Investigator Merck Research Laboratories WP75B 1305 PO Box 4 West Point PA 19486 From Gastonguay

    Original URL path: http://nonmem.org/nonmem/nm/99mar292006.html (2016-04-25)
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  • [NMusers] restraining eta code
    S If you run this code THETA7 0 7 ETA4 rnorm 100000 mean 0 sd sqrt 6 4 PHI log THETA7 1 THETA7 P exp PHI ETA4 1 exp PHI ETA4 hist P p95CI quantile P probs c 0 025 0 975 you will see that the distribution for P is not normal at all and 95 confidence intervals are essentially from 0 to 1 2 5 97 5 0 01641659 0 99714220 Leonid From nplock zedat fu berlin de Subject Re NMusers restraining eta code Date Tue 28 Mar 2006 15 24 00 0200 CEST Dear Leonid thanks a lot What about the coefficient of variation If I calculate it back from the obtained confidence intervals THETA7 97 5 quantile 2 I would end up with values between 21 and 28 So could one say that the coefficient of variation is approx 25 for example Nele From Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers restraining eta code Date Tue 28 Mar 2006 15 25 05 0200 Hi Nele The CV is approximately OMEGA ETA 4 THETA 7 1 THETA 7 Where OMEGA is SD not variance Best regards 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 Leonid Gibiansky leonidg metrumrg com Subject Re NMusers restraining eta code Date Tue 28 Mar 2006 08 37 35 0500 Nele I do not think it is appropriate to describe the P distribution in terms of sd cv etc just look on the histogram that is U shaped with the peaks at near 0 and 1 Individual P can be anywhere between 0

    Original URL path: http://nonmem.org/nonmem/nm/99mar282006.html (2016-04-25)
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  • [NMusers] ADVAN6 coding question
    1 75 3 1 80 1 1 80 3 1 85 1 1 85 3 1 90 1 1 90 3 2 1 0 1 2 1 0 3 2 0 5 1 2 0 5 3 2 1 1 2 1 3 2 1 5 1 2 1 5 3 2 2 1 2 2 3 2 2 5 1 2 2 5 3 2 3 1 2 3 3 2 3 5 1 2 3 5 3 2 4 1 2 4 3 2 4 5 1 2 4 5 3 2 5 1 2 5 3 2 5 5 1 2 5 5 3 2 6 1 2 6 3 2 6 5 1 2 6 5 3 2 7 1 2 7 3 2 7 5 1 2 7 5 3 2 8 1 2 8 3 2 10 1 2 10 3 2 12 1 2 12 3 2 14 1 2 14 3 2 16 1 2 16 3 2 18 1 2 18 3 2 20 1 2 20 3 2 22 1 2 22 3 2 24 1 2 24 3 2 26 1 2 26 3 2 28 1 2 28 3 2 30 1 2 30 3 2 32 1 2 32 3 2 34 1 2 34 3 2 36 1 2 36 3 2 38 1 2 38 3 2 40 1 2 40 3 2 45 1 2 45 3 2 50 1 2 50 3 2 55 1 2 55 3 2 60 1 2 60 3 2 65 1 2 65 3 2 70 1 2 70 3 2 75 1 2 75 3 2 80 1 2 80 3 2 85 1 2 85 3 2 90 1 2 90 3 3 3 0 1 3 1 0 3 3 0 5 1 3 0 5 3 3 1 1 3 1 3 3 1 5 1 3 1 5 3 3 2 1 3 2 3 3 2 5 1 3 2 5 3 3 3 1 3 3 3 3 3 5 1 3 3 5 3 3 4 1 3 4 3 3 4 5 1 3 4 5 3 3 5 1 3 5 3 3 5 5 1 3 5 5 3 3 6 1 3 6 3 3 6 5 1 3 6 5 3 3 7 1 3 7 3 3 7 5 1 3 7 5 3 3 8 1 3 8 3 3 10 1 3 10 3 3 12 1 3 12 3 3 14 1 3 14 3 3 16 1 3 16 3 3 18 1 3 18 3 3 20 1 3 20 3 3 22 1 3 22 3 3 24 1 3 24 3 3 26 1 3 26 3 3 28 1 3 28 3 3 30 1 3 30 3 3 32 1 3 32 3 3 34 1 3 34 3 3 36 1 3 36

    Original URL path: http://nonmem.org/nonmem/nm/99mar242006.html (2016-04-25)
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  • [NMusers] Effect of FOCE vs FOCE INTER on success of covariance step within a model with additive error
    methods cound not converge For datasets on a thin line between convergence or not convergence results to converge using FOCE might not be consist with those using FOCE INTER INTER option requires many calculations for matrix so there might be a delicate difference of a convergent result between FOCE INTER and FOCE methods Takuya Okagaki Statistical Analysis Section Clinical Research Planning Coordination Department Clinical Research Division Tanabe Seiyaku Co Ltd e mail okagaki tanabe co jp tel 81 6 6205 5835 fax 81 6 6205 5223 From Xu Xu xu xu bms com Subject Re NMusers Effect of FOCE vs FOCE INTER on success of covariance step within a model with additive error Date Fri 10 Mar 2006 09 43 08 0500 Hi When I get this error massage I usually set MATRIX S in COV and then the covariance step would run successfully But this doesn t work for R MATRIX SINGULAR Could someone explain what is the reason for this What is the difference between R and S matrices Thanks a lot Xu From Gastonguay Marc marcg metrumrg com Subject Re NMusers Effect of FOCE vs FOCE INTER on success of covariance step within a model with additive error Date Fri 10 Mar 2006 10 31 57 0500 Hi Sameer These results are a bit puzzling but I ll offer a possible explanation below The NONMEM documentation for INTERACTION states The dependence on etas of the model for intra individual random error is preserved in the computation of the objective function In your case there is no ETA EPS interaction because the individual prediction is not involved in the calculation of the residual variance but INTERACTION may still play a small role I did investigate a couple of additive residual model examples and although the starting OFV min

    Original URL path: http://nonmem.org/nonmem/nm/99mar092006.html (2016-04-25)
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  • [NMusers] Help on a cell transit model
    23 2003 18 25 4 299 0 0 1 55181 1 1 09 71 49 21 1004 201 1004 0 5 01 24 2003 9 40 4 934 0 0 1 36098 1 0 88 71 54 22 1004 201 1004 0 5 01 24 2003 10 17 4 960 1 1 1 12000 6000 0 88 71 54 23 1004 201 1004 0 5 01 24 2003 14 15 5 125 0 0 1 65632 1 0 88 71 54 24 1004 201 1004 0 5 01 24 2003 18 14 5 291 0 0 1 69745 1 0 88 71 54 25 1004 201 1004 0 9 01 28 2003 15 15 9 167 0 0 1 24703 1 0 03 71 74 26 1004 201 1004 0 9 01 28 2003 18 00 9 281 0 0 0 03000 7 0 03 71 74 27 1004 201 1004 0 16 02 04 2003 15 15 16 167 0 0 0 06188 1 0 05 72 09 28 1004 201 1004 0 30 02 18 2003 18 00 30 281 0 0 0 10000 7 0 10 72 80 29 1004 201 1004 0 79 04 08 2003 18 00 79 281 0 0 0 37000 7 0 37 72 80 30 1004 201 1004 0 177 07 15 2003 18 00 177 281 0 0 0 46000 7 0 46 72 80 When I run the control stream in simulation mode the very first record at time 0 for the first subject gives initial estimates in all cell count compartments A3 to A7 as zero Also the next subject 1006 has at time zero not 0 amounts in the compartments but the last values for the subject prior Obs ID visit days TIME IPRED DV EVID CMT MDV A3 A4 A5 A6 A7 1 1004 0 0 000 0 00 0 00000 1 7 1 0 0000 0 0000 0 0000 0 0000 0 00000 2 1004 0 0 281 6 75 1 73510 0 0 7 0 2 1139 2 1139 2 1139 2 1139 1 73510 3 1004 0 8 281 198 75 0 48713 0 0 7 0 2 1139 2 1139 2 1139 2 1139 0 48713 4 1004 0 29 281 702 75 1 23790 0 0 7 0 2 1139 2 1139 2 1139 2 1139 1 23790 5 1004 0 78 283 1878 80 1 67740 0 0 7 0 2 1139 2 1139 2 1139 2 1139 1 67740 6 1004 0 176 283 4230 80 1 99880 0 0 7 0 2 1139 2 1139 2 1139 2 1139 1 99880 7 1004 0 260 283 6246 80 2 07540 0 0 7 0 2 1139 2 1139 2 1139 2 1139 2 07540 8 1004 12 364 283 8742 80 1 24820 0 0 7 0 2 1139 2 1139 2 1139 2 1139 1 24820 9 1004 12 371 283 8910 80 0 67630 0 0

    Original URL path: http://nonmem.org/nonmem/nm/99feb282006.html (2016-04-25)
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