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Re: Linear VS LTBS

From: Leonid Gibiansky <LGibiansky>
Date: Fri, 21 Aug 2009 18:42:15 -0400

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.


Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
e-mail: LGibiansky at
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
> ( 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)
>> 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:
>> e-mail: LGibiansky at
>> 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
>>> 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.Grevel
>>> <mailto:Joachim.Grevel
>>> 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
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>>> Bhattacharya
>>> *Sent:* 20 August 2009 17:07
>>> *To:* nmusers
>>> *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
Received on Fri Aug 21 2009 - 18:42:15 EDT

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