@Ruben@Jakob Very worthwhile discusstion! I would like to raise an
extended question: if the model contains one covariate, the values of which
from external data make parameters negative, what would be the optimal
solution for this?

@Ruben Out of curiosity, why did you use Nelder-Mead method instead of
others in your software? And what do you mean OFIM?

​Met vriendelijke groet
,
T
​G

On Tue, Apr 10, 2018 at 3:19 PM, Jakob Ribbing <jakob.ribbing@pharmetheus
.com> wrote:

> Hi Ruben,
>
> I think I misread Tingjies original posting as taking ABS(ETA), whereas
> his initial attempt was actually ABS(1+ETA), which is less problematic.
> The latter would not bias simulations much if IIV is e.g. 30% CV, agreed.
>
> However, as Tingjies is mainly interested in estimation, I believe that
> without the ABS-correction, no subject will have the EBE at ETA <= -1 for a
> parameter that could not be <=0.
> Unless possibly in a subject which is a) uninformative on that parameter
> and b) where the eta is also part of an omega-block - a scenario which
> seems unlikely to me, but may occur in theory.
>
> Implementing the ABS-korrection ETA=-1.2 would give the same solution
> (parameter value) as ETA=-0.8, but at a higher OFV for that subject.
> It seems to me, if negative parameter values are only a problem in the eta
> search for the EBE, whereas the EBE for individual parameters are always
> positive, then it should be more straightforward to use FOCE, with the
> addition e.g.:
> IF(PARA.LT.0.001) PARA=0.001
> Probably, no subject will have such a low individual parameter value, when
> looking into the table output?
> If there are any such subjects I would look for errors in the data set and
> nonmem code (as outlined in my initial reply).
>
> The above concerns estimation.
> In simulation (unless %CV is low), we may get a fraction of subject with
> PARA=0.001, which may be an unreasonably low parameter value.
> Whether that is acceptable or not depends on the objectives and in this
> case there was no need for simulations even for model evaluation (?), so I
> will not elaborate further.
>
> Cheers
>
> Jakob
>
>
>

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