The correct URL for outside access to the cognigen archive is:
http://www.cognigencorp.com/nonmem/nm/99apr242002.html
http://www.cognigencorp.com/nonmem/nm/99jan071999.html
Nick
[EMAIL PROTECTED] wrote:
>
> Navin,
>
> Another model that can be applied in the log-transofrmed domain is
> documented in:
>
> http://huxley.phor.com/nonmem/nm/99apr242002.html
>
> and
>
> http://huxley.phor.com/nonmem/nm/99jan071999.html
>
> It has similar properties of the ADD+PROP in the log domain. The
> concentrations that are low are weighted less. In fact since it is in
> the log domain, the concentrations that are high are weighted lower as
> well, meaning the middling concentrations have the highest weight. It
> is mentioned in:
>
> SL Beal. /Ways to Fit a PK Model with Some Data Below the
> Qunatification Limit/ J. Pharmacokin.Pharamcodyn. 28, p. 481-504.
>
> It is given in Equation 11. He states
>
> "Logrithmically tranformed ... observations whos pharmacokientic
> predictions become theretically small, but both their centraltendency
> and variance seem to remain constant and above certain levels (assuming
> that the assay is accurate, this can only happen with the kinetics are
> misspecified), in which case another useful model for the logrithmically
> transformed observations is ... (EQ 11 here) .. where m is an extra
> positively constraned parameters."
>
> Just FYI.
>
> Matthew Fidler
> [EMAIL PROTECTED]
>
> navin goyal wrote:
>
> > 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
> >
> >
> >
> > --
> > --Navin
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090
www.health.auckland.ac.nz/pharmacology/staff/nholford