NONMEM Users Network Archive

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Re: ETAs & SIGMA in external validation

From: Tingjie Guo <iam>
Date: Fri, 13 Apr 2018 17:20:40 +0200

   
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?

  
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
.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 f=
or 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 et=
a
> 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, whe=
n
> looking into the table output?
> If there are any such subjects I would look for errors in the data set an=
d
> 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
>
>
>

Received on Fri Apr 13 2018 - 11:20:40 EDT

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