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
