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.
Hi all,
I saw this snippet of information on PsN-general mailing list.
Kajsa Harling wrote in PsN-general:
I talked to the experts here about shrinkage. Apparently, sometimes an
individual's eta may be exactly 0 (no effect, placebo, you probably
understand this better than I do). These zeros
Hi Pyry,
Yes, when calculating shrinkage or looking at eta-diagnostic plots it is often
better to exclude etas from subjects that has no information on that parameter
at all. For a PK model we would not include subjects that were only
administered placebo (if PK is exogenous compound). In
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
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
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
Hi Pyry and Jacob,
If you exclude zero etas then what happens to infomative individuals who just
happen to have the population typical values?
This approch would exclude these individuals when trying to indicate how
informative an estimation is about a parameter.
I know this is unlikely, but
Hi Douglas,
This has been a concern for me as well, although I do not know if this ever
happens(?). For the automatic (generic scripts) exclusion of etas that I use
for eta-diagnostics, I tend to exclude a group (e.g. each dose or dose-study
combination) if all subjects have eta=0 in that
Dear Leonid, I have followed the law of parsimony in the model and have used
ETAS on VC and CL only. There was no correlation between the parameters and
between the ETAS. The R square values were less than 0.2.
I did not try the combined error model yet and will do so.
Some concentrations values
Hello Jakob, et al.
I would agree that individuals who do not contribute data to the
estimation of a particular element of OMEGA should be excluded from
the ETA-shrinkage calculation or ETA-based diagnostics. I think that
using individual ETA=0 as the filtering criterion may be a
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
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
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