Nick, We recently have come across a very sqewed residual distribution (easily seen in placebo data, where there was no placebo effect) that we modeled as additive + proportional in the log domain. Additive + proportional error in untransformed domain was worse. We have not tried more complex error models in the untransformed domain, so it is not a clean comparison, but for practical purposes, yes, there may be situations when log transformation is still useful even with INTER.
Katya ------------------- Ekaterina Gibiansky Senior Director, PKPD, Modeling & Simulation ICON Development Solutions ekaterina.gibian...@iconplc.com -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Nick Holford Sent: Friday, August 21, 2009 4:44 PM To: nmusers Subject: Re: [NMusers] Linear VS LTBS Leonid, You are once again ignoring the actual evidence that NONMEM VI will fail to converge or not complete the covariance step more or less at random. If you bootstrap simulated data in which the model is known and not overparameterised it has been shown repeatedly that NONMEM VI will sometimes converge and do the covariance step and sometimes fail to converge. Of course, I agree that overparameterisation could be a cause of convergence problems but I would not agree that this is often the reason. Bob Bauer has made efforts in NONMEM 7 to try to fix the random termination behaviour and covariance step problems by providing additional control over numerical tolerances. It remains to be seen by direct experiment if NONMEM 7 is indeed less random than NONMEM VI. BTW in this discussion about LTBS I think it is important to point out that the only systematic study I know of comparing LTBS with untransformed models was the one you reported at the 2008 PAGE meeting (www.page-meeting.org/?abstract=1268). My understanding of your results was that there was no clear advantage of LTBS if INTER was used with non-transformed data: "Models with exponential residual error presented in the log-transformed variables performed similar to the ones fitted in original variables with INTER option. For problems with residual variability exceeding 40%, use of INTER option or log-transformation was necessary to obtain unbiased estimates of inter- and intra-subject variability." Do you know of any other systematic studies comparing LTBS with no transformation? Nick Leonid Gibiansky wrote: > Neil > Large RSE, inability to converge, failure of the covariance step are > often caused by the over-parametrization of the model. If you already > have bootstrap, look at the scatter-plot matrix of parameters versus > parameters (THATA1 vs THETA2, .., THETA1 vs OMEGA1, ...), these are > very informative plots. If you have over-parametrization on the > population level, it will be seen in these plots as strong > correlations of the parameter estimates. > > Also, look on plots of ETAs vs ETAs. If you see strong correlation > (close to 1) there, it may indicate over-parametrization on the > individual level (too many ETAs in the model). > > For random effect with a very large RSE on the variance, I would try > to remove it and see what happens with the model: often, this (high > RSE) is the indication that the error effect is not needed. > > Also, try combined error model (on log-transformed variables): > > W1=SQRT(THETA(...)/IPRED**2+THETA(...)) > Y = LOG(IPRED) + W1*EPS(1) > > > $SIGMA > 1 FIXED > > > Why concentrations were on LOQ? Was it because BQLs were inserted as > LOQ? Then this is not a good idea. > Thanks > Leonid > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > > Indranil Bhattacharya wrote: >> Hi Joachim, thanks for your suggestions/comments. >> >> When using LTBS I had used a different error model and the error >> block is shown below >> $ERROR >> IPRED = -5 >> IF (F.GT.0) IPRED = LOG(F) ;log transforming predicition >> IRES=DV-IPRED >> W=1 >> IWRES=IRES/W ;Uniform Weighting >> Y = IPRED + ERR(1) >> >> I also performed bootsrap on both LTBS and non-LTBS models and the >> non-LTBS CI were much more tighter and the precision was greater than >> non-LTBS. >> I think the problem plausibly is with the fact that when fitting the >> non-transformed data I have used the proportional + additive model >> while using LTBS the exponential model (which converts to additional >> model due to LTBS) was used. The extra additive component also may be >> more important in the non-LTBS model as for some subjects the >> concentrations were right on LOQ. >> >> I tried the dual error model for LTBS but does not provide a CV%. So >> I am currently running a bootstrap to get the CI when using the dual >> error model with LTBS. >> >> Neil >> >> On Fri, Aug 21, 2009 at 3:01 AM, Grevel, Joachim >> <joachim.gre...@astrazeneca.com >> <mailto:joachim.gre...@astrazeneca.com>> wrote: >> >> Hi Neil, >> 1. When data are log-transformed the $ERROR block has to >> change: >> additive error becomes true exponential error which cannot be >> achieved without log-transformation (Nick, correct me if I am >> wrong). >> 2. Error cannot "go away". You claim your structural model >> (THs) >> remained unchanged. Therefore the "amount" of error will remain the >> same as well. If you reduce BSV you may have to "pay" for it with >> increased residual variability. >> 3. Confidence intervals of ETAs based on standard errors >> produced >> during the covariance step are unreliable (many threads in NMusers). >> Do bootstrap to obtain more reliable C.I.. >> These are my five cents worth of thought in the early morning, >> Good luck, >> Joachim >> >> >> ------------------------------------------------------------------------ >> >> AstraZeneca UK Limited is a company incorporated in England and >> Wales with registered number: 03674842 and a registered office at 15 >> Stanhope Gate, London W1K 1LN. >> >> *Confidentiality Notice: *This message is private and may contain >> confidential, proprietary and legally privileged information. If you >> have received this message in error, please notify us and remove it >> from your system and note that you must not copy, distribute or take >> any action in reliance on it. 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No contractual relationship is created >> by this message by any person unless specifically indicated by >> agreement in writing other than email. >> >> *Monitoring: *AstraZeneca UK Limited may monitor email traffic data >> and content for the purposes of the prevention and detection of >> crime, ensuring the security of our computer systems and checking >> compliance with our Code of Conduct and policies. >> >> -----Original Message----- >> >> >> *From:* owner-nmus...@globomaxnm.com >> <mailto:owner-nmus...@globomaxnm.com> >> [mailto:owner-nmus...@globomaxnm.com >> <mailto:owner-nmus...@globomaxnm.com>]*On Behalf Of *Indranil >> Bhattacharya >> *Sent:* 20 August 2009 17:07 >> *To:* nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> >> *Subject:* [NMusers] Linear VS LTBS >> >> Hi, while data fitting using NONMEM on a regular PK data set >> and its log transformed version I made the following >> observations >> - PK parameters (thetas) were generally similar >> between >> regular and when using LTBS. >> -ETA on CL was similar >> -ETA on Vc was different between the two runs. >> - Sigma was higher in LTBS (51%) than linear (33%) >> Now using LTBS, I would have expected to see the >> ETAs unchanged >> or actually decrease and accordingly I observed that the eta >> values decreased showing less BSV. However the %RSE for ETA on >> VC changed from 40% (linear) to 350% (LTBS) and further the >> lower 95% CI bound has a negative number for ETA on Vc (-0.087). >> What would be the explanation behind the above >> observations >> regarding increased %RSE using LTBS and a negative lower bound >> for ETA on Vc? Can a negative lower bound in ETA be considered >> as zero? >> Also why would the residual vriability increase when using LTBS? >> Please note that the PK is multiexponential (may be >> this is >> responsible). >> Thanks. >> Neil >> >> -- Indranil Bhattacharya >> >> >> >> >> -- >> Indranil Bhattacharya >> -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand n.holf...@auckland.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090 mobile: +64 21 46 23 53 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford ICON plc made the following annotations. ------------------------------------------------------------------------------ This e-mail transmission may contain confidential or legally privileged information that is intended only for the individual or entity named in the e-mail address. 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