RE: Linear VS LTBS

From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Sunday, August 23, 2009 11:02 PM To: Leonid Gibiansky Cc: nmusers Subject: Re: [NMusers] Linear VS LTBS Leonid, This is what I wanted to bring to the attention of nmusers: "Of course, I agree that overparameterisation could be a cause of convergence problems but I would not agree that this is often the reason. " If you can provide some evidence that over-paramerization is *often* the cause of convergence problems then I will be happy to consider it. What kind of evidence did you have in mind? My experience with NM7 beta has not convinced me that the new methods are helpful compared to FOCE. They require much longer run times and currently mysterious tuning parameters to do anything useful. Truly exponential error is never the truth. This is a model that is wrong and IMHO not useful. You cannot get sensible optimal designs from models that do not have an additive error component. All models are wrong and I see no reason why the exponential error model would be different although I think it is better than the proportional error for most situations. It seems that you assume that whenever TBS is used, only an additive error (on the transformed scale) is used. Is that why you say it is wrong? Or is it because you believe in negative concentrations? Why would you not be able to get sensible information from models that don't have an additive error component? (You can of course have a residual error magnitude that increases with decreasing concentrations without having to have an additive error; this regardless of whether you use the untransformed or transformed scale). Nick Leonid Gibiansky wrote: Hi Nick, You are once again ignoring the actual evidence that NONMEM VI will fail to converge or not complete the covariance step for over-parametrized problems :) Sure, there are cases when it doesn't converge even if the model is reasonable, but it does not mean that we should ignore these warning signs of possible ill-parameterization. I think that the group is already tired of our once-a-year discussions on the topic, so, let's just agree to disagree one more time :) Nonmem VII unlike earlier versions will provide you with the standard errors even for non-converging problems. Also, you will always be able to use Bayesian or SAEM, and never worry about convergence, just stop it at any point and do VPC to confirm that the model is good :) Yes, indeed, I observed that FOCEI with non-transformed variables was always or nearly always equivalent to FOCEI in log-transformed variables. Still, truly exponential error cannot be described in original variables, so I usually try both in the first several models, and then decide which of them to use fro model development. Thanks Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Nick Holford wrote: 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 <[email protected] <mailto:[email protected]> <mailto:[email protected]>> 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. Any unauthorised use or disclosure of the contents of this message is not permitted and may be unlawful. *Disclaimer:* Email messages may be subject to delays, interception, non-delivery and unauthorised alterations. Therefore, information expressed in this message is not given or endorsed by AstraZeneca UK Limited unless otherwise notified by an authorised representative independent of this message. 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:* [email protected] <mailto:[email protected]> <mailto:[email protected]> [mailto:[email protected] <mailto:[email protected]> <mailto:[email protected]>]*On Behalf Of *Indranil Bhattacharya *Sent:* 20 August 2009 17:07 *To:* [email protected] <mailto:[email protected]> <mailto:[email protected]> *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 [email protected] 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