RE: OMEGA matrix
Dear Pavel,
To answer your question I suggest you go on Bob Bauer's NONMEM 7 course. The
understanding I gleaned from that course (which I think was enhanced by the
excellent wine we had at lunch in Alicante) was that with appropriate MU
parameterisation there is virtually no computational disadvantage to estimating
the full block with the newer algorithms. So you might as well do it, at least
in early runs where you want an idea of which parameter correlations might be
useful/reasonably estimated.
BW,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
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From: [email protected] [[email protected]] On Behalf Of
Ken Kowalski [[email protected]]
Sent: 25 September 2014 22:43
To: 'Eleveld, DJ'; 'Pavel Belo'; [email protected]
Subject: RE: [NMusers] OMEGA matrix
Warning: This message contains unverified links which may not be safe. You
should only click links if you are sure they are from a trusted source.
Hi Douglas,
My own thinking is that you should fit the largest omega structure that can
be supported by the data rather than just always assuming a diagonal omega
structure. This does not necessarily mean always fitting a full block omega
structure, as it can often lead to an ill-conditioned model, however, there
may be a reduced block omega structure that is more parsimonious than the
diagonal omega structure. Getting the omega structure right is particularly
important for simulation of individual responses. For example, if you
always simulate from a diagonal omega structure for CL and V when there is
evidence that the random effects are highly positively correlated then you
may end up simulating individual PK profiles for combinations of individual
CLs and Vs that are not represented in your data (i.e., high correlation
would suggest that individuals with high CL will tend to also have high V
and vice versa whereas a simulation assuming that they are independent will
result in simulating for some individuals with high CL and low V and some
individuals with low CL and high V that might not be represented in your
data). This could lead to simulations that over-predict the variation in
the concentration-time profiles even though the diagonal omega may be
sufficient for purposes of predicting central tendency in the PK profile.
You can confirm this by VPC looking at your ability to predict say the 10th
and 90th percentiles in comparison to the observed 10th and 90th percentiles
in your data. That is, if you simulate from the diagonal omega when there
is correlation in the random effects you may find that your prediction of
the 10th and 90th percentiles are more extreme than that in your observed
data. I see this all the time in VPC plots where the majority of the
observed data are well within the predictions of the 10th and 90th
percentiles when we should expect about 10% of our data above the 90th
percentile prediction and 10% below the 10th percentile prediction.
Best regards,
Ken
Kenneth G. Kowalski
President & CEO
A2PG - Ann Arbor Pharmacometrics Group, Inc.
110 Miller Ave., Garden Suite
Ann Arbor, MI 48104
Work: 734-274-8255
Cell: 248-207-5082
Fax: 734-913-0230
[email protected]
www.a2pg.com
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Eleveld, DJ
Sent: Thursday, September 25, 2014 4:36 PM
To: Pavel Belo; [email protected]
Subject: RE: [NMusers] OMEGA matrix
Hi Pavel,
My question is: Why is it desirable to fit a complete omega matrix if its
physical interpretation is unclear? Etas are variation of unknown origin
i.e. not explained by the structural model. A full omega matrix allows the
unknown variation of one paramater to have a (linear?) relationship with
some other thing that is also unknown. If unknown A is found to have a
linear relationship with unknown B, then what knowlegde is gained? I do
think it can be instructive to to look at correlations and use this
information to make a better structural model. But I think diagonal OMEGA
matrix is more desirable if it works ok.
warm regards,
Douglas Eleveld
________________________________________
From: [email protected] [[email protected]] on behalf
of Pavel Belo [[email protected]]
Sent: Thursday, September 25, 2014 4:24 PM
To: [email protected]
Subject: [NMusers] OMEGA matrix
Hello Nonmem Community,
It seems like NONMEM developers may advise to start with full OMEGA matrix
at the beginning of model development. Monolix developers may advise to
start with a diagonal matrix. Is there something different in NONMEM SAEM
algorithms that makes model stable when a lot of statistically insignificant
correlations/covariances are estimated in the model?
It seems like NONMEM SAEM can be very stable in very “hard cases” (a lot of
outliers, partially misspecified model, overparameterized model, etc.). The
omega matrix is a part of the puzzle.
When it is impossible to test every correlation coefficient for significance
due to some limitations, it becomes a regulatory issue. We may need to be
able to make a statement that the model is safe and sound even when OMEGA
matrix can be overparameterized (tries to estimate too many insignificant
parameters within the OMEGA matrix).
Kind regards,
Pavel
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