RE: Error model
In regard to Navin's original problem, the most likely cause (in most
datasets) is that you have some very small F's (NONMEM predictions) at
the late time-points, e.g. log (.0001)=-9.2. If your observed value was
say .01, this is a hundred-fold difference so the residual is equal to
about -4.6. A few of these can inflate your sigma estimate
substantially. The next question is not can you fix it, but whether you
should. The fix is simply to reset these F to a higher level (e.g half
BQL) but this involves creating a discontinuity in the first derivative
(e.e a sudden change in the relationship between F and the weighting of
a data point, which slightly compromises the original purpose of the
log-transformation). If there is significant additive error, then it
is better to estimate the model on the absolute scale.
In Navin's case the pre-dose time-points (96h post-dose for a drug with
a "4h half-life") should be zero unless there is a second compartment.
If they are all BQL, it would not be unreasonable to discard them (set
MDV=1). The dataset sound very sparse, particularly as end-of-infusion
time-points are notoriously noisy in practise.
Best regards, James
James G Wright PhD
Scientist
Wright Dose Ltd
Tel: 44 (0) 772 5636914
www.wright-dose.com http://www.wright-dose.com/
Quoted reply history
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of navin goyal
Sent: 04 October 2007 21:20
To: nmusers
Subject: [NMusers] Error model
Dear Nonmem users,
I am analysing a POPPK data with sparse sampling
The dosing is an IV infusion over one hour and we have data for time
points 0 (predose), 1 (end of infusion) and 2 (one hour post infusion)
The drug has a half life of approx 4 hours. The dose is given once every
fourth day
When I ran my control stream and looked at the output table, I got some
IPREDs at time predose time points where the DV was 0
the event ID EVID for these time points was 4 (reset)
(almost 20 half lives)
I was wondering why did NONMEM predict concentrations at these time
points ?? there were a couple of time points like this.
I started with untransformed data and fitted my model.
but after bootstrapping the errors on etas and sigma were very high.
I log transformed the data , which improved the etas but the sigma shot
upto more than 100%
( is it because the data is very sparse ??? or I need to use a better
error model ???)
Are there any other error models that could be used with the log
transformed data, apart from the
Y=Log(f)+EPS(1)
Any suggestions would be appreciated
thanks
--
--Navin