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 ken.kowal...@a2pg.com www.a2pg.com -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Eleveld, DJ Sent: Thursday, September 25, 2014 4:36 PM To: Pavel Belo; nmusers@globomaxnm.com 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: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] on behalf of Pavel Belo [non...@optonline.net] Sent: Thursday, September 25, 2014 4:24 PM To: nmusers@globomaxnm.com 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 ________________________________ De inhoud van dit bericht is vertrouwelijk en alleen bestemd voor de geadresseerde(n). Anderen dan de geadresseerde(n) mogen geen gebruik maken van dit bericht, het niet openbaar maken of op enige wijze verspreiden of vermenigvuldigen. Het UMCG kan niet aansprakelijk gesteld worden voor een incomplete aankomst of vertraging van dit verzonden bericht. The contents of this message are confidential and only intended for the eyes of the addressee(s). Others than the addressee(s) are not allowed to use this message, to make it public or to distribute or multiply this message in any way. The UMCG cannot be held responsible for incomplete reception or delay of this transferred message.
