NONMEM Users Network Archive

Hosted by Cognigen

Re: Linear VS LTBS

From: Nick Holford <n.holford>
Date: Mon, 24 Aug 2009 09:06:14 +1200

Katya,

I have no doubt one can find examples that show TBS is better than no
transformation. But as Leonid demonstrated that is not a consistent
property of TBS.

I did not say that TBS was not useful -- however I have not seen any
evidence to say it generally preferable to no transformation. TBS brings
its own practical problems so I am rarely motivated to use it.

Nick

Gibiansky, Ekaterina wrote:
> 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.Gibiansky
>
>
> -----Original Message-----
> From: owner-nmusers
> 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.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
>>>
>>>
>>>
>>>
> ------------------------------------------------------------------------
>
>>> 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:* owner-nmusers
>>> <mailto:owner-nmusers
>>> [mailto:owner-nmusers
>>> <mailto:owner-nmusers
>>> 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
>>>
>>>
>
>

--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
n.holford
mobile: +64 21 46 23 53
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Received on Sun Aug 23 2009 - 17:06:14 EDT

The NONMEM Users Network is maintained by ICON plc. Requests to subscribe to the network should be sent to: nmusers-request@iconplc.com.

Once subscribed, you may contribute to the discussion by emailing: nmusers@globomaxnm.com.