Re: [NMusers] Error model

 Hi Navin,
You could try both additive and proportional error model
$ERROR

   TY=F

   IF(F.GT.0) THEN

   TY=LOG(F)

   ELSE

   TY=0

   ENDIF

   IPRED=TY
   W=SQRT(THETA(n-1)**2+THETA(n)**2/IPRED**2) ; log transformed data
    Y=TY+W*EPS(1)



$SIGMA 1 FIX

Best,

Nidal



Nidal Al-Huniti, PhD

Strategic Consulting Services

Pharsight Corporation

nal-huniti_at_pharsight.com




On 10/4/07, navin goyal <navin1180_at_gmail.com> 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
> thanks
>
> --
> --Navin

Received on Fri Oct 05 2007 - 03:12:43 EDT