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RE: Linear VS LTBS

From: Grevel, Joachim <Joachim.Grevel>
Date: Fri, 21 Aug 2009 08:01:27 +0100

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,

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-----Original Message-----
From: 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 =

Indranil Bhattacharya

Received on Fri Aug 21 2009 - 03:01:27 EDT

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