Re: [NMusers] Error model

James,
Note that EVID=4 at those time points, so they could not influence
variance of the residual error, and do not influence parameter estimates.
Leonid

James G Wright wrote:
> In regard to Navin's original problem, the most likely cause (in most
> datasets) is that you have some very small F's (NONMEM predictions) at
> the late time-points, e.g. log (.0001)=-9.2. If your observed value was
> say .01, this is a hundred-fold difference so the residual is equal to
> about -4.6. A few of these can inflate your sigma estimate
> substantially. The next question is not can you fix it, but whether you
> should. The fix is simply to reset these F to a higher level (e.g half
> BQL) but this involves creating a discontinuity in the first derivative
> (e.e a sudden change in the relationship between F and the weighting of
> a data point, which slightly compromises the original purpose of the
> log-transformation). If there is significant additive error, then it
> is better to estimate the model on the absolute scale.
>
> In Navin's case the pre-dose time-points (96h post-dose for a drug with
> a "4h half-life") should be zero unless there is a second compartment.
> If they are all BQL, it would not be unreasonable to discard them (set
> MDV=1). The dataset sound very sparse, particularly as end-of-infusion
> time-points are notoriously noisy in practise.
>
> Best regards, James
>
>
>
>
> James G Wright PhD
> Scientist
> Wright Dose Ltd
> Tel: 44 (0) 772 5636914
> www.wright-dose.com <http://www.wright-dose.com/>
>
> -----Original Message-----
> *From:* owner-nmusers_at_globomaxnm.com
> [mailto:owner-nmusers_at_globomaxnm.com] *On Behalf Of *navin goyal
> *Sent:* 04 October 2007 21:20
> *To:* nmusers
> *Subject:* [NMusers] Error model
>
> 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 - 08:24:48 EDT