RE: Linear VS LTBS
Hi Neil,
Well if you compare proportional+additive error model with a logarithmic error
model then it shouldnt be suprising that they work differently and give you
different residual variance. Logarithmic error model presumes that the
accuracy of the observations, in absolute terms, becomes very good for low
concentrations. With real-world (i.e. not simulated) measuments this might not
be the case and this is probably the motivation for the proportional+additive
type models. The best error model is the one that best matches the
characteristics of the very(!) complex physical process behind the reporting of
some number as "concentration of substance X in the sample".
If a proportional+additive error model works better than a logarithmic error
model then I would check to see if the observations at small concentrations
(usually the late observations) are possibly dominating the estimation for the
logarithmic model. These samples influence the estimation less for
propotional+additive error model because the additive term.
If you have many observations close to LOQ then there are a number of different
suggestion in the literature how to handle these. I wouldnt make any
conclusions about the best error model until you have decied how you are going
to handle them.
There was some recent discussion on this list about the possibility of a
logarithmic+additive model. It was complicated and I didnt really follow it.
Douglas Eleveld
________________________________
Quoted reply history
Van: [email protected] namens Indranil Bhattacharya
Verzonden: vr 21-8-2009 13:52
Aan: Grevel, Joachim
CC: [email protected]
Onderwerp: Re: [NMusers] Linear VS LTBS
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]> 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]]on Behalf Of Indranil Bhattacharya
Sent: 20 August 2009 17:07
To: [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