Leonid, I have never reported out as a final model a run that failed to converge or failed the COV step. My guess is that individuals who frequently do probably tend to be more mechanistic in their model building than I am and often push the complexity of their models beyond what can be supported by the data in hand. For those that do report out models that don't converge, I wonder if they have tried re-running their models with different starting values (15-20% different) and see if NONMEM fails to converge at the same set of parameter estimates. My guess is in many cases it won't although both sets of estimates may appear "reasonable" and give similar fits and VPC.
For individuals who have strong prior beliefs about their mechanistic models, my thinking is that rather than using approximate maximum likelihood methods and ignoring the diagnostics that might suggest their model is unstable or not fully supported by the data, I think they would be better served by using a Bayesian approach. That way they can be explicit about the strength of their priors and they don't have to worry about convergence and COV step failures. JMHO. Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 E. Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 [EMAIL PROTECTED] -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Leonid Gibiansky Sent: Friday, November 21, 2008 3:53 PM To: Mark Sale - Next Level Solutions Cc: nmusers Subject: Re: [NMusers] Models that abort before convergence Addendum Mark, "Useful" is the relative and subjective term. Error messages and convergence information are useful to me (i.e., they make my search of the final model more efficient), and I'd like to understand whether they are useful to other people. I do not try to prove that the model completed without error messages is correct, or that the model completed with rounding error is wrong, or whether the error messages provide information not readily available in NPC, NPDE and PPC. I am interested to see how many people find it useful: full stop here, do not try to interpret the poll beyond this simple statement. In addition, questions 4-7 will help us to understand how widespred is the use of models with failed convergence step and/or with failed minimization step. Thanks Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 Mark Sale - Next Level Solutions wrote: > > Leonid, > Let me understand: > You now have a theory that the way to determine whether the NONMEM error > messages are useful (i.e., they tell you something about the model > "goodness") is a poll. This, I think is a theory (and one well > established in epistomolgy) of how to find an optimal solution - appeal > to a large number of presumably well informed people. As data that may > be relavant to this theory, I would point out that a poll gave us GW > Bush as our 43rd president. > Nick, in contrast has suggested that the error messages could be used > as a source of random numbers. This also, I think, is a theory without > data to support or contradict it. > So .... > Let me propose a solution - let's generate some data. Suppose we > randomly generate 1000 models. We could tests the hypotheses: > > Are the error messages random (I suspect they are not, that there is > some information in them). To test this, see if the error messages are > predictive of other (presumably non-random) measure of goodness - NPC > and NPDE, and perhaps PPC come to mind. > > Do the error messages provide information not readily available in NPC, > NPDE and PPC. > Not really sure how to test this, without some "gold standard" of > goodness, except perhaps to compare the different measures to the model > that was used to simulate the data (seems like measures based on that > would be "correct" in some way??). I need some ideas on this. > > > I can generate, run and extract results from random models (using the GA > software) - I already have NPDE and PPC in it, was thinking of adding NPC. > > Any interest/collaborators?? > > > > > Mark Sale MD > Next Level Solutions, LLC > www.NextLevelSolns.com <http://www.NextLevelSolns.com> > 919-846-9185 > > -------- Original Message -------- > Subject: Re: [NMusers] Models that abort before convergence Addendum > From: Leonid Gibiansky <[EMAIL PROTECTED]> > Date: Thu, November 20, 2008 9:57 pm > To: > Cc: nmusers <[email protected]> > > Nick, Mark, and All, > We can argue indefinitely, but let me propose a poll. If you like to > participate, reply directly to me (use "reply", not "reply to all"). I > will summarize all the replies received up to the end of November. Skip > the questions that you do not like to answer, write NA if the question > is not applicable. Summaries will be blinded. > > 1. Would you like Nonmem to stop producing all run-time (not syntax) > error/warning messages (134, 137, number of significant digits, etc.) > and "MINIMIZATION SUCCESSFUL" messages (YES/NO): > > 2. Do you remember at least one example when the run-time error message > helped you to find an error in your code (YES/NO): > > 3. In your experience, run-time error messages allow you to detect > model > errors or problems quicker than it would be done without error > messages: > (agree/disagree) > > 4. Have you ever used in your report/publication ANY model that did not > have $COV step completed (YES/NO): > > 5. Have you ever used in your report/publication ANY model that did not > converge (YES/NO): > > 6. Have you ever used in your report/publication FINAL model that did > not have $COV step completed (YES/NO): > > 7. Have you ever used in your report/publication FINAL model that did > not converge (YES/NO): > > 8. Define yourself as novice/intermediate/experienced Nonmem user: > > Thanks > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com <http://www.quantpharm.com> > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > >
