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RE: Linear VS LTBS

From: Gibiansky, Ekaterina <Ekaterina.Gibiansky>
Date: Fri, 21 Aug 2009 18:49:14 -0400

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
>>
>>
>>
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>> -----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

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Received on Fri Aug 21 2009 - 18:49:14 EDT

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