Dear All,
Is it possible to fix all of the THETAs, OMEGAs and SIGMAs to their
estimated values, and then re-run the model with all of one type of data
in, /but the other type of data retained for just a single individual/?
This would give an objective function contribution per individual, for
each type of data I think, which could be repeated across all
individuals (and data types). Essentially, you would leave all of one
type of data in the dataset, but have the other type of data only for
the individual in question and then we wouldn't have the
within-individual correlation problem highlighted by Jeroen. And we
would end up with a set of individual contributions for each data type,
which account for the "driving" effect of the other data type.
The reason I think this is the best we can do is that all of the
information across individuals is in the global parameters, and it's the
within individual correlation across the two endpoints that is really
tricky to account for. And with one type of data retained for all
subjects, we can then see a difference for the other type as we iterate
across subjects, while retaining the within subject information provided
by the first data type. Fundamentally, this still isn't quite separate
as one type of data will alter the objective function contribution (i.e.
likelihood) of the other i.e. adding PD for one individual can alter the
objective function contribution from that individual's PK (and move the
posthocs) - but that is because at a fundamental level, you cannot
separate the objective function contributions of two observations that
depend on each other!
Note that when these individual contributions are summed, they certainly
won't add up to the original objective function, partly as there will be
some padding dropped from the NONMEM objective function. However the
padding should solely be a function of the number of data points (not
their actual values), so the difference between the individual totals
summed and the all data objective function could be divided by the
number of observations, and a compensation applied to each individual
objective function value based on the percentage of all observations in
that individual.
I think this is as close as we can get to a contribution per data type
(with the driving effect of the other data type retained). Now it is
possible that this approach will turn out to be inferior to Jeroen's
suggestion of running the model on one type of data with the other
models post-hocs, which would be much quicker. By breaking it down
among individuals, when you compare across models, you will sometimes
find that some individuals benefit a great deal from a model change, and
dominate the objective function.
Kind regards, James
PS It is also possible to access these objective function contributions
by calculating individual contributions one at a time for each
observation type and then both observation types (because all of the
global population information is already in the THETAs, OMEGAs and
SIGMAs), which uses much less computational power, however I couldn't
write that down in a way that was easy to follow.
https://www.popypkpd.com
On 11/10/2022 10:44, Matts Kågedal wrote:
Thanks Mats,
Sounds great and like a lot of work, let me know when you have
implemented the ability to do this in simeval :)
Best,
Matts
On Mon, Oct 10, 2022 at 10:45 PM Mats Karlsson
<mats.karls...@farmaci.uu.se> wrote:
Hi Matts,
One opportunity to learn about the expected fit of a model to data
in relation to the actual fit is to use the PsN functionality
“simeval”. In this functionality multiple data sets are simulated
from the final model and the realized design. The OFV per subject
(and the overall OFV) can be assessed after evaluation (i.e.,
MAXEVAL=0) or estimation of each of the simulated data sets. This
will provide reference OFV distributions with which the real data
OFV (subject or total study population) can be compared in a PPC
like manner. This is developed from Largajolli et al.
(https://www.page-meeting.org/default.asp?abstract=3208).
In your case, you are interested in learning about the relative
contribution of the different variables of a joint model. While
not having tried it, I imagine that you can, based on your final
joint model(s), obtain the expected OFV distribution one variable
at a time as well as them jointly. From this it ought to be
possible to learn some about the quality of the model with respect
to variable A, variable B and their joint distribution in
describing the real data.
Best regards,
Mats
*From:*owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com>
*On Behalf Of *Stephen Duffull
*Sent:* den 10 oktober 2022 21:56
*To:* Jeroen Elassaiss-Schaap (PD-value) <jer...@pd-value.com>
*Cc:* Matts Kågedal <mattskage...@gmail.com>; nmusers@globomaxnm.com
*Subject:* RE: [NMusers] OFV by endpoint of joint models?
HI Jeroen
I tested this with additive error (i.e. interaction has no
influence) and combined. Rank order was not preserved.
To be clearer, this was a PK only example and I compared
sum(CIWRES^2) for each individual vs PHI(). I was trying to see
if I could get the PHI() per analyte for a multiple response model
and thought that a quick way of doing this was to grab the
relative contribution from CIWRES.
Cheers
Steve
*From:*Jeroen Elassaiss-Schaap (PD-value) <jer...@pd-value.com
<mailto:jer...@pd-value.com>>
*Sent:* Tuesday, 11 October 2022 8:36 am
*To:* Stephen Duffull <stephen.duff...@otago.ac.nz
<mailto:stephen.duff...@otago.ac.nz>>
*Cc:* Matts Kågedal <mattskage...@gmail.com
<mailto:mattskage...@gmail.com>>; nmusers@globomaxnm.com
<mailto:nmusers@globomaxnm.com>
*Subject:* Re: [NMusers] OFV by endpoint of joint models?
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
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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
<mailto:owner-nmus...@globomaxnm.com><owner-nmus...@globomaxnm.com
<mailto: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
<mailto:mattskage...@gmail.com>>; nmusers@globomaxnm.com
<mailto: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
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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
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