Re: [NMusers] Linear VS LTBS

Fri, 21 Aug 2009 15:06:08 -0700 

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]>> 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


    
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        -----Original Message-----


        *From:* [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]>
        *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
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