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
