Hi Al,
Serge's suggestion is available in practise,however when we are considering
to add one covariate such as body weight to the parameter-CL,e.g.,the number of
off-diagonal elements retained in the base model may be different from the one
in the covariate model .As I have noticed,one or above off-diagonal elements
could cross the zero cutoff again and should be excluded from the OMEGA block
structure.So it is hardly to keep the constructure of OMEGA block same during
the model improvment .
In my opinion,if all the diagonal elements fall in an acceptable
interval,such as the CV of parameter is within 50%,there is no need to insert
the off-diagonal element.The off-diagonal element which means covariance
between the diagonal paramete,represents the correlation between them.So
another way is to refer to the scatter plots between ETAs estimated by the
model with diagonal elements .Which off-diagonal element is included depends
on the correlation between two ETAs in the scaterr plots.
Most frequently,the diagonal elements are enough.Do not be worried about that.
By the way, when we discussed the off-diagonal issue,we should not forget the
basic purpose of model building-to make the model predictive performance to be
in accordance with the observed values as far as possible.
Jeroen,
Do you mean the off-diagonal elements instead of diagonal elements when you
mentioned in the second paragraph,because I would like to believe the the
off-digonal elements are more difficult to estimate
hongbo ye
from nanjing city.
2010-08-26
yhb5442387
发件人: "Serge Guzy" <[email protected]>
发送时间: 2010-08-26 04:46
主 题: RE: [NMusers] Block versus diagonal omega
收件人: "Berg, Alexander K., Pharm.D., Ph.D." <[email protected]>,
<[email protected]>
I am not sure there is one single statistical test you can use like we do with
covariate selection (forward followed by backward deletion method).
The easiest way to deal with this problem would be first to use a stable method
like importance sampling assisted by MAP estimation (IMPMAP in NONMEM7) and
getting the full variance covariance matrix and correlation matrix. NONMEM7
will give you also like SADAPT the standard errors associated with each
correlation coefficient. A way to categorize these correlation coefficients
would be to look at each correlation mean +- 2 standard errors and see if it
crosses the zero cutoff. If so, you would assume this correlation not to be
statistically significant. Once all the not statistically significant
correlations are deleted, you have your new blocks to be considered (I guess
you have sometimes to change the order of your parameters to define this new
block in NONMEM7) and you refit your model with this new blocks. Of course,
this is an approximation but at least it allows you ranking the most important
correlations based on both their mean but also their corresponding standard
errors.
A pure diagonal variance covariance matrix will affect the outcome of your
subsequent simulations and usually would inflate the response variability
across the population as important correlations are may be missing.
Serge Guzy; Ph.D
President, CEO; POP_PHARM; INC;
www.poppharm.com
[email protected]
510 684 87 40
From: [email protected] [mailto:[email protected]] On
Behalf Of Berg, Alexander K., Pharm.D., Ph.D.
Sent: Wednesday, August 25, 2010 12:20 PM
To: [email protected]
Subject: [NMusers] Block versus diagonal omega
Hello -
I was curious if someone from the group could perhaps describe the basis for
deciding whether to use a block (variance and covariance) versus diagonal
(variance only) form of omega. Specifically, what tests if any can be
performed to decide between the two forms and are there certain situations
where one is preferred over the other as I often see only the diagonal form
used. Any help would be much appreciated -
Al Berg, PhD/PharmD
Clinical Pharmacology Fellow
Mayo Clinic - Rochester
[email protected]
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