RE: OMEGA matrix

Van: Jeroen Elassaiss-Schaap [mailto:[email protected]] Verzonden: September 30, 2014 1:00 AM Aan: [email protected]; [email protected]; [email protected]; [email protected]; Eleveld, DJ Onderwerp: Re: [NMusers] OMEGA matrix Dear Pavel, others, The underlying technical difference is that SAEM is in its core a sampling methodology. Off-diagonal elements (as explained by Bob Bauer) are available as sample correlations and do not have to be separately computed in contrast to linearization approaches such as FOCE. The more interesting question to me, as also eluted to by Ken, is what criteria to set up for inclusion of an off-diagonal element. I completely support his argument for simulation performance of the model, as e.g. judged using a VPC. Whether to score it as an additional degree of freedom may be up to debate. An off-diagonal element in essence limits the freedom of the model as the random space in which samples can be generated will be smaller. In that perspective one could argue to retain any off-diagonal element that is sufficiently deviating from zero regardless of ofv changes, and to not apply the concept of over-parametrization (or at least not in comparison to other types of parameters). In practice inclusion of an important off-diagonal is mostly accompanied by a sound improvement in ofv anyway. More can be found in earlier discussions we had on this list, see e.g. https://www.mail-archive.com/[email protected]/msg02736.html https://www.mail-archive.com/[email protected]/msg02736.html for quite an extensive one from 2010. Here also an r-script to visualize the parameter space impact can be found ;-). In cases where a larger full or banded omega block is found, I would advice to explore its properties further using matrix decomposition approaches (PCA etc) to evaluate propagated correlations across the matrix. But also on the basis of physiology/pharmacology as a data sample may not be informative enough to support robust interpretation of correlations. A discussion along those lines in reporting seems the more fruitful to me. Best regards, Jeroen http://pd-value.com -- More value out of your data! -----Original Message----- From: [email protected]<mailto:[email protected]> [mailto:[email protected]] On Behalf Of Standing Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUST) Sent: Friday, September 26, 2014 09:15 To: Kowalski, Ken; 'Eleveld, DJ'; 'Pavel Belo'; [email protected]<mailto:[email protected]> Subject: RE: [NMusers] 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 ________________________________ From: [email protected]<mailto:[email protected]> [[email protected]<mailto:[email protected]>] On Behalf Of Ken Kowalski [[email protected]<mailto:[email protected]>] Sent: 25 September 2014 22:43 To: 'Eleveld, DJ'; 'Pavel Belo'; [email protected]<mailto:[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]<mailto:[email protected]> http://www.a2pg.com -----Original Message----- From: [email protected]<mailto:[email protected]> [mailto:[email protected]] On Behalf Of Eleveld, DJ Sent: Thursday, September 25, 2014 4:36 PM To: Pavel Belo; [email protected]<mailto:[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]<mailto:[email protected]> [[email protected]<mailto:[email protected]>] on behalf of Pavel Belo [[email protected]<mailto:[email protected]>] Sent: Thursday, September 25, 2014 4:24 PM To: [email protected]<mailto:[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 ________________________________