@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 > > >