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RE: Linear VS LTBS

From: Grevel, Joachim <Joachim.Grevel>
Date: Fri, 21 Aug 2009 13:08:07 +0100

Neil,
 
I never use what you call the "dual error" model with log-transformed =
data as I do not undertstand all the assumptions that would be implied. =
You can refine your $ERROR block further by incorporating the LOQ. The =
moxonidine models of Mats Karlsson's group (also in their workshop =
material or their publications) show you how it is coded.
 
Sorry, I do not have the time to find them and paste them into this =
mail.
 
Joachim


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-----Original Message-----
From: Indranil Bhattacharya [mailto:ibhattacharya
Sent: 21 August 2009 12:53
To: Grevel, Joachim
Cc: nmusers
Subject: 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 < =
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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have received this message in error, please notify us and remove it from =
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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.



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


From: owner-nmusers
owner-nmusers
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 - 08:08:07 EDT

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