Re: [NMusers] Error model

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
matthew.fidler_at_cognigencorp.com

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

Received on Fri Oct 05 2007 - 13:34:54 EDT