Hi Steven, Thanks for sharing! CWRES is “polluted” by the ETA gradients more directly compared to OFV. One would however hope for rank order consistency. Did you also test this without interaction? Might also be interesting to test the other residuals that nonmem offers in that respect.
Cheers Jeroen http://pd-value.com jer...@pd-value.com @PD_value +31 6 23118438 -- More value out of your data! > Op 10 okt. 2022 om 18:51 heeft Stephen Duffull <stephen.duff...@otago.ac.nz> > het volgende geschreven: > > Hi Jeroen > > I note your thought about CWRES and OFV. In some exploratory work, we did > not find that the rank order of abs(CIWRES) or CIWRES^2 and PHI() was > preserved (with FOCEI) for continuous data. I had anticipated some rank > similarity. > > Cheers > > Steve > ________________________________________ > Stephen Duffull | Professor > Otago Pharmacometrics Group > School of Pharmacy | He Rau Kawakawa > University of Otago | Te Whare Wānanga o Otāgo > Dunedin | Ōtepoti > Aotearoa New Zealand > Ph: 64 3 479 5099 > > > > > > -----Original Message----- > From: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf > Of Jeroen Elassaiss-Schaap (PD-value B.V.) > Sent: Tuesday, 11 October 2022 4:07 am > To: Matts Kågedal <mattskage...@gmail.com>; nmusers@globomaxnm.com > Subject: Re: [NMusers] OFV by endpoint of joint models? > > Hi Matts, > > The easiest way to assess is when one of two endpoints is modeled directly > (TTE, logistic regression) as often is the case, than look at the Y value for > those endpoints, as reported in the PRED variable. The sum of those values is > the ofv, or proportional to it, for that particular endpoint - the other > endpoint is than affected in the inverse way. > > If you have multiple continuous endpoints it becomes more complicated. > You could either look at the sum of absolute CWRES to get an idea, but not > exact in terms of ofv comparison. Another approximate comparison would be to > run the model without evaluation (e.g. MAXEVAL=0) with the original msfofile > as $MSFI for the separate endpoints (by e.g. > IGN(DVID.NE.x) where x is your endpoint). It is not exact, again, as it > ignores the correlation between endpoints but should get you in the > neighborhood. As an improvement to this method you could force evaluation at > the original posthocs by reading them in in your datafile > - this would still ignore correlation but the effect would be largely > diminished because the posthocs are fixed to those estimated with correlation. > > Hope this helps, > > Jeroen > > https://apc01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fpd-value.com%2F&data=05%7C01%7Cstephen.duffull%40otago.ac.nz%7C1ebbd4a5cbce449913f108daaad22217%7C0225efc578fe4928b1579ef24809e9ba%7C0%7C0%7C638010116756566698%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=PEGSjbNvoRzu%2B6AbaXCXTHlNGkGc28Eb7QgKu1tE9sM%3D&reserved=0 > jer...@pd-value.com > @PD_value > +31 6 23118438 > -- More value out of your data! > >> On 10-10-2022 16:03, Matts Kågedal wrote: >> Hi all, >> I have a question related to the objective function value when >> multiple endpoints are modelled jointly. Specifically I would like to >> know if a change in in OFV between models is driven primarily by one >> of the endpoints or if both contributes to the change, or maybe they >> are even driving the OFV in oposite directions. >> >> Is there a way to get some form of partial OFV by endpoint? >> Best regards, >> Matts > >
