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- [NMusers] WinPOPT Release 1.1 beta now available

the WinPOPT site please do so when you download the update Registration gives you earlier access to updates design and WinPOPT tips When installing WinPOPT release 1 1 we recommend that you do so in a different directory from release 1 thereby allowing both versions to be used The new version contains many fixes for bugs that appeared unexpectedly in version 1 It is also more stable and gives far

Original URL path: http://nonmem.org/nonmem/nm/99oct152006.html (2016-04-25)

Open archived version from archive - [NMusers] PRED in $TABLE from rich and sparse data

the PRED concentrations in the TABLE should be same between subjects When dealing with reach data I got various PRED concentrations in the TABLE between subjects But when the data are sparse I got almost same PRED concentrations in the TABLE between subjects Thanks Jim Jiang From Serge Guzy Subject Re NMusers PRED in TABLE from rich and sparse data Date Thu 12 Oct 2006 I am not an expert

Original URL path: http://nonmem.org/nonmem/nm/99oct122006.html (2016-04-25)

Open archived version from archive - [NMusers] Some basic modeling results

which I wish to discuss with him 1 that the FO method tends to provide more biased estimates of population mean parameters than FOCE And 2 that if we fit a between subject variance model Omega that is too simple i e diagonal when it should be including covariances then our estimates of the variability of the fixed effects parameters and predictions of mean responses are likely to be underestimated Are these two things true Can you show me literature references about this Thank you in advance for helping me with my homework Susan From Nick Holford n holford auckland ac nz Subject Re NMusers Some basic modeling results Date Fri 06 Oct 2006 10 09 37 1300 Susan Evidence for the bias in FO can be found in the earliest publications describing NONMEM s performance with simulated data I cannot at the moment produce a published reference which directly compares the two estimation methods I expect someone else can do that The second issue is trickier I have been working on a simulation project for some years now with Diane Mould and Joga Gobburu that has tried to answer your question We have still not reached a fully satisfactory

Original URL path: http://nonmem.org/nonmem/nm/98oct052006.html (2016-04-25)

Open archived version from archive - [NMusers] ANN: Census 1.0b1

of R are required as well but not included The usual disclaimer applies this is a beta release as opposed to earlier releases which were pre betas and so minor bugs are almost certainly present Census is and will remain completely free under the Mozilla Public License version 1 1 Please send any comments feature requests complaints and bug reports to me as usual The more feedback I get the

Original URL path: http://nonmem.org/nonmem/nm/99oct052006.html (2016-04-25)

Open archived version from archive - [NMusers] systematic difference

4 Oct 2006 16 53 21 0500 Dear NMUSERS I am using NONMEM to do population PK analysis I am wondering how to consider the systematic difference between two clinical sites Whether I can consider the systematic difference using the residual error model A combined error model is used Whether I can code the systematic difference like this Y F EXP EPS 1 EPS 2 SITE EPS 3 1 SITE

Original URL path: http://nonmem.org/nonmem/nm/99oct042006.html (2016-04-25)

Open archived version from archive - [NMusers] Outlier in subject level

observation level What is the general rule to identify exclude outlier in subject level For example I use FOCEI to fit the pooled data from phase 1 and phase 2 The minimization was successful However the box plot and histogram of the individual CL and V suggested that two subjects clearly have very different values from the population typical CL is about 47 L h but the CL in these

Original URL path: http://nonmem.org/nonmem/nm/99oct022006.html (2016-04-25)

Open archived version from archive - [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION

suggested most of the time outliers are model dependent i e outliers occurred because the appropriate model wasn t or couldn t be fitted Actually I don t think I said this My general impression which doesn t particularly influence how I handle these cases is that once we ve done a good job on the modeling the remaining outliers are most likely errors in data which would occur under any reasonable model Further you wrote This can only be assessed by analyzing data in two ways including and excluding the outliers Although I think that such contrasting analyses can be of great help to understand the impact of anomalous data my point was the opposite you don t necessarily have to do these contrasting analyses if you use a model that is more robust to outliers errors 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 Nandy Partha PRDUS PNandy prdus jnj com Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Wed 27 Sep 2006 07 15 30 0400 Hi All Thanks for discussing this outlier issue It is always very difficult to decide whether to keep the outlying data points in or remove those I have a question though For Additive error models I think what Mats suggested works great What is your suggestion for a ADD PROP Error models Should one use OMEGAs on both ADD and PROP Errors Also bear in mind that if one is using AIC or any other such criteria to select models one needs to now account for additional parameters I am interested in your opinion Kind Regards Partha From Chuanpu Hu sanofi aventis com Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Wed 27 Sep 2006 09 25 53 0400 Hi Mats Sorry for not interpreting you correctly thank you for clearifying I agree with your second point and I generally don t go about deleting outliers My point was that if one does delete outliers then the impact should be assessed In particular I have seen assessments been reported with changes in 2LL which I think is misleading Regarding your first point I think sometimes in practice a good job is not done for various reasons For example we know that dosing or sample collection times are inexact in phase III trials Modeling can be attempted fot this but not easilly Could you comment on searching and dealing with outliers in this situation Best regards Chuanpu From Kowalski Ken Ken Kowalski pfizer com Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Wed 27 Sep 2006 15 27 55 0400 Hi Mats Nmusers Here are my two cents on this discussion 1 For individual data point outliers wouldn t the ETA on Epsilon residual error model you propose effectively down weight all of the observations within an individual and not just the suspected outlier data point I certainly see value in the ETA on Epsilon residual error model when the magnitude of the residual variation does not appear to be the same across all subjects However in using this model I would want to assess whether the apparent change in magnitude of the residual variation across subjects is being unduly influenced by a single observation within the subject s data If it is I don t think I would use this approach Of course it may be a challenge to discrimate statistical outliers vs misspecification of the residual error model e g non homogenous variation across subjects vs lack of fit of the structural model Note that a change in residual error model to accommodate outliers rather than excluding outliers is making an implicit set of assumptions so I don t think we can side step the issue of outlier assessment we are just trading one set of assumptions for another 2 Matt Hutmacher and I have been toying with the following idea to address individual data outliers First based on a prespecified set of criteria identify suspected individual data outliers Second create a flag variable on the data set to identify these data outliers i e FLAG 1 denotes outlier FLAG 0 denotes non outlier Third fit a residual error model with different sigmas for outliers and non outliers The following code for a constant CV error model might be considered Y F 1 1 FLAG EPS 1 FLAG EPS 2 If the outliers appear to be independent of F then one might postulate EPS 2 as an additive effect With this model sigma2 would be larger than sigma1 effectively down weighting the suspected outliers without having to formally exclude them i e giving zero weight to them The degree of down weighting can be determined from the ratio of the estimates of sigma2 to sigma1 and would increase as the magnitude of outlier deviations increases One could compare the parameter estimates thetas and omegas from this model to that of the usual CV error model Y F 1 EPS 1 to determine how much leverage these outliers collectively have on the estimation Any thoughts on this approach We don t have any direct experience in applying this approach so if anyone would like to try it and report back their experiences we would certainly be interested in hearing about it 3 For detecting individual data point outliers as opposed to outlying subjects wouldn t the IWRES be a better diagnostic than WRES or CWRES It would seem to better fit with the sentiment that when assessing individual data point outliers they should be evaluated in context with the other observations for that individual presumably with respect to their deviations from the IPRED 4 Outlier assessment is a very contextual thing It is nearly impossible to be completely objective in this assessment but at the same time we should be systematic and use sound reasoning in evaluating outliers and the actions we take While we need to be cautious when considering the impact of exclusion or down weighting individual outliers we also shouldn t take the position that we should never exclude them These outliers can unduly inflate the variance components and mask our ability to detect important determinants covariate effects of the PK and PD responses We need to rigorously evaluate the adequacy of our models with various diagnostic plots and rule out whenever possible various forms of model structural and statistical misspecification before proposing to exclude outliers The totality of our diagnostics should help inform our decision on the models we postulate and any actions including no action we take regarding outliers Ken From Dr Sima Sadray Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Wed 27 Sep 2006 Dear All I think the article below will help Likelihood based diagnostics for Influential Individuals in nonlinear mixed effects model selection S Sadray E N Jonsson and M O Karlsson Pharmaceutical Research Vol 16 No 8 1999 Sima Dr Sima Sadray PharmD PhD Division of Pharmacokinetics and Biopharmaceutics Department of Pharmaceutics Faculty of Pharmacy Tehran University of Medical Sciences P O Box 14155 6451 Tehran IRAN Telfax 98 21 66959054 Mobile 0912 2022793 Fax 98 21 66461178 E mail sadrai sina tums ac ir From Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Wed 27 Sep 2006 23 54 18 0200 Ken Regarding your comments 1 I agree According to the model I suggested a single outlying data point would mean that the entire information content of that individual would be considered less than without that outlier Of course this model makes assumptions too even if it relaxes the assumption of everyone having the same residual variability It still makes the assumption that residual error distribution is a transformation of normal distribution 2 Maybe the idea has merits Trying it and showing that the extra subjectivity and effort does pay off in terms of increased parameter precision is however something that I think needs to be shown Also with your approach I would think that even when you do identify the outliers correctly the assumption of a normally distributed random error for the outliers is usually not appropriate In my experience that is not what outliers errors look like E g often some observations are far too high sampling in the wrong arm or far too low didn t take the dose but rarely do the two equate to form a nice normal Further I m not sure what you mean by prespecified criteria This could be tricky as outliers are usually not easy to foresee Your suggestion seems to imply that these are identified before you fit a model to the data and then it is even harder to predict which are outliers Last it is not uncommon that one can see that one out of two data points are an outlier but difficult to determine which of the two it is 3 I tend to agree but IWRES is not a panacea either If data are sparse compared to the number of parameters and especially etas IWRES can be quite misleading due to overfit 4 Your usual good advice that I would not want to disagree with In relation to this one of my former co workers Dr Sima Sadrai reminded me in a mail that I think was intended for nmusers copied below that there may be some further help in inspecting the individual contribution to the likelihood The idea is to investigate whether some individuals are driving or masking any model selection The main idea was not in relation to errors outlying data points but maybe it has some merits there too 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 Elassaiss Schaap J Jeroen jeroen elassaiss organon com Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Thu 28 Sep 2006 08 18 30 0200 Hi Ken To my opinion your idea of flagging with an extra epsilon is a first step towards complete iterative weighting as demonstrated by Jan Freijer see http www page meeting org page page2005 PAGE2005P76 pdf I guess he won t chime in himself His implementation has two pros i it is unsupervised and ii it is gradual He specified the example of errors in dosing and sampling time but the approach seems general and therefore also applicable to other causes of outliers I have used a similar approach during my PhD but in another field with smoothing rather than fitting i e adaptive smoothing It worked really well in removal of electronic artifacts observed in noisy densily sampled time series and was simple to implement iteration on a linear regression Best regards Jeroen J Elassaiss Schaap Scientist PK PD Organon NV PO Box 20 5340 BH Oss Netherlands Phone 31 412 66 9320 Fax 31 412 66 2506 e mail jeroen elassaiss organon com From Kowalski Ken Ken Kowalski pfizer com Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Thu 28 Sep 2006 17 08 59 0400 Mats We appear to be in good agreement on all points Thank you for your kind words regarding 4 and info on Dr Sadray s et al paper I will certainly take a look at it I just have a follow up with regards to your responses to 2 2 Certainly work needs to be done to evaluate whether this approach indeed has merit I agree there is no reason necessarily to believe outliers are normal however we most likely will lack suitable power to assess the distribution of these outlying data There is precedence to consider a mixture model of normal distributions referred to in the statistical literature as a contaminated normal distribution where Y is distributed as 1 p N mu sigma p N mu k sigma where p represents the fraction of outliers and k is the scale parameter for the increased variation in the outliers see Barnett and Lewis Outliers in Statistical Data Wiley 1978 pp 31 33 127 130 We propose a two stage approach to this contanimated normal distribution by first estimating p by use of a prespecified outlier criteria and fixing this through the use of the FLAG variable In the second stage we estimate k which is the ratio of sigma2 to sigma1 The outlier criteria which would ideally be specified in the analysis plan before starting the model development might be something like flag all data points as potential outliers for further evaluation where abs IWRES 5 perhaps a reasonable criteria with dense data Of course we could look at a full likelihood mixture model approach were p and k are simultaneously estimated There are other contaminated normal mixture models that allow for asymmetry a shift in mu as well as a scale increase in sigma and of course mixtures of different distributions between non outliers and outliers Whether we have enough power to discern between various contaminated distributions and how well they may perform in the context of PK PD is certainly an area that could benefit from some research Kind regards Ken From Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Fri 29 Sep 2006 16 45 56 0200 Hi Ken Good luck with the evaluations When you write Of course we could look at a full likelihood mixture model approach were p and k are simultaneously estimated do you know a software that could do that NONMEM would not be able to handle it 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 Kowalski Ken Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Fri 29 Sep 2006 12 17 45 0400 Mats It can be done in NONMEM credit goes to Matt Hutmacher for figuring this out You need to use the LIKELIHOOD or 2LL option Here is an example of a PRED code segment that Matt Hutmacher prepared and tested PRED MU THETA 1 ETA 1 MP THETA 2 SIG1 THETA 3 SIG2 THETA 4 IW1 DV MU SIG1 IW2 DV MU SIG2 L1 0 5 LOG 2 3 14159265 LOG SIG1 0 5 IW1 2 L2 0 5 LOG 2 3 14159265 LOG SIG2 0 5 IW2 2 L 1 MP EXP L1 MP EXP L2 Y 2 LOG L Note that MU can be replaced with a more complex PK PD model MP is the mixing probability SIG1 is the sigma for non outliers and SIG2 is the sigma for outliers Regards Ken From Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Sat 30 Sep 2006 17 38 50 0200 Ken Nice I guess it does not come without a price as the same mixture model is applied to subjects with and without outliers alike If one were to take this estimation route estimating a interindividual mixture model for the mixing component would be a way to address this MIX P 1 THETA 5 Proportion of subjects with outliers P 2 1 P 1 Proportion of subjects without outliers PRED MU THETA 1 ETA 1 MP THETA 2 MP for subjects with outliers IF MIXNUM EQ 2 MP 0 MP for subjects without outliers SIG1 THETA 3 SIG2 THETA 4 IW1 DV MU SIG1 IW2 DV MU SIG2 L1 0 5 LOG 2 3 14159265 LOG SIG1 0 5 IW1 2 L2 0 5 LOG 2 3 14159265 LOG SIG2 0 5 IW2 2 L 1 MP EXP L1 MP EXP L2 Y 2 LOG L If you have really contaminated data maybe you in addition want to add a logit transformed ETA on MP for subjects with outliers 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 Nick Holford n holford auckland ac nz Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Mon 02 Oct 2006 12 59 33 1300 Mats Ken I must be missing some subtle issue here why do you think it is necessary to code this using 2LL Why not code it like this THETA 0 0 1 1 P prob of being an outlier 0 1 SD of additive residual error 0 1 K fractional difference in SD in outlier population OMEGA 0 5 between subject variability in MYPRED SIGMA 1 FIX unit random effect MIX NSPOP 2 P 1 THETA 1 P prob of being an outlier P 2 1 P 1 PRED MYPRED any PKPD model you like with random effects e g MYPRED DOSE V EXP CL V TIME ETA 1 SD any residual error model you want expressed with THETAs e g SD THETA 2 additive residual error SD IF MIXNUM EQ 1 THEN outlier KOUT THETA 3 ELSE KOUT 1 ENDIF Y MYPRED KOUT SD EPS 1 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 Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers WRES AND OUTLIER IDENTIFICATION EXCLUSION Date Mon 2 Oct 2006 08 25 22 0200 Hi Nick The intention is to have the

Original URL path: http://nonmem.org/nonmem/nm/99sep252006.html (2016-04-25)

Open archived version from archive - [NMusers] Bootstrap question

interpreting the results Ignoring the treatment design and doing a non parametric bootstrap across the whole data set would probably inflate the CI a bit For typical quantitative applications of the CI e g testing hypotheses for model building then this bias would tend to make you be a little bit more parsiminous and conservative before accepting new parameters in the model In my experience it is unusual that anyone would really care much about the bias of a CI for the parameter estimates Its just something one does to get some rough idea of the uncertainty and to satisfy obsessional journal reviewers editors 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 Leonid Gibiansky leonidg metrumrg com Subject Re NMusers Bootstrap question Date Fri 22 Sep 2006 19 03 13 0400 If kinetics is linear it should not matter If you observe or would like to investigate dose effect I would keep relative fractions of arm relatively stable for example by sampling within each group as you suggested Leonid From Chuanpu Hu sanofi aventis com Subject Re NMusers Bootstrap question Date Tue 26 Sep 2006 Very well said Nick Although when we did this we really cared Chuanpu From Nick Holford n holford auckland ac nz Subject Re NMusers Bootstrap question Date Tue 26 Sep 2006 08 32 00 1200 Chuanpu I d be interested to know why you really cared about the bias in a bootstrap CI Its hard to imagine a situation when one might care for the CI on PK parameters but perhaps you were really interested in a PD parameter Nick

Original URL path: http://nonmem.org/nonmem/nm/99sep222006.html (2016-04-25)

Open archived version from archive