Svetlana,
The answer depends on whether you would like to allow your effect to be
negative or not.
If yes, you need to rewrite the model so that the model allows negative
predictions, for example,
EFF = TBAS + TSLP*CP**TALP
where TSLP is allowed to be negative.
If not, then you can use the model that produces only the positive
productions. In this case, negative values will be treated as a random
noise (you need to have additive error component in this case), e.g.,:
Y=EFF+EPS(1)
or
Y=EFF*(1+EPS(1))+EPS(2)
Thanks
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
On 9/21/2011 8:25 AM, Vinogradova Svetlana wrote:
Dear NONMEM users,
I'm analyzing a dataset of 4 patients, there are 2 types of observations for each patient
- vehicle (say baseline) and dose (treatment). We want to analyze our dataset in terms of
(treatment-baseline). So I've modified my dataset, and now I have only one
"column" of observations for each patient: DV= (treatment-baseline). But now I
have some problems because I have some negitive values in DV column. In case of some
models it results in problems, for example:
TEFF = TBAS + (TSLP*CP)**TALP
so TSLP should be positive but it's not that right...
Maybe there are some common methods to deal with this case?
Thanks a lot,
Svetlana
