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RE: Minimization terminated ?

From: Mark Sale - Next Level Solutions <mark>
Date: Fri, 20 Jul 2007 18:04:58 -0700
    I'm interested in exactly what you mean by "unreliable".  Is it sensitivity/specificity for a "bad" model?  I suspect that we all would prefer if our models converge and have a successful covariance step.   And so (I think), models that pass these tests are "better" models than those that don't (everything else being equal).  But, if we are unable to find a model that passes these tests, we resort to rationalizing that it really doesn't make any difference, anyway, and so I can move on.  You, I, and others have generated data that support this.  On the other hand, Stuart would, I'm pretty sure, suggest that models that fail a covariance step should not be considered final, and would cringe at the idea of accepting as final a model that did not converge.  I'd also suggest it might be hurdle in getting a paper published.  (I'll let the regulatory agencies speak for themselves on this matter)  So, I'd suggest that convergence and a covariance step are valuable information and should not be discarded.
 But, I very much support the value of visual predictive checks, and NPDE.  I'd like to add PPC, especially if one checks both a point estimate (AUC, Cmax, Cmin) and some measure of variability (SE of AUC etc), since an artificially large variability can fool PPC.

Mark Sale MD
Next Level Solutions, LLC

-------- Original Message --------
Subject: Re: [NMusers] Minimization terminated ?
From: Nick Holford <n.holford Date: Fri, July 20, 2007 5:04 pm
To: nmusers <nmusers

NONMEM is quite unreliable when it comes to deciding if it has
converged. Minor changes in initial estimates with essentially no
difference in the final estimates and OBJ can produce 1) SUCCESSFUL +

My guess this is because of numerical rounding errors (not the ones
that NONMEM refers to in its error message) so that essentially it
becomes a random event which of these outcomes you get. The bottom
line is NOT to pay attention to NONMEM's declarations of success but
to focus on whether the parameters make sense, whether the fits look
good, does a VPC look OK and even (if you have got lots of spare time) does the npde fail to reject the null. Several investigations of bootstraps have shown that it makes little difference if you include successful runs only or if you include all runs. The advantage of all runs is that is simpler to process the results and perhaps the confidence intervals are more precisely estimated because you have more runs. Nick navin goyal wrote: > > Dear NM Users, > I am using trying to model some POPPK data in NONMEM vi > Sometimes I get the following message in the output file > > MINIMIZATION TERMINATED > DUE TO ROUNDING ERRORS (ERROR=134) > NO. OF FUNCTION EVALUATIONS USED: 1103 > NO. OF SIG. DIGITS UNREPORTABLE > > But when I change the SIGDIGITS to a lower value the minimization is successful. What exactly is happening in this case ? Is there something I am missing out? > > what about the parameter estimates obtained in such a run ? > > Another question related to this is that when I bootstrap a model in wings for nonmem WFN, I get few runs with similar message where in it also says the same message as above ..MINIMIZATION TERMINATED DUE TO ROUNDING ERRORS (ERROR=134) NO. OF SIG. DIGITS UNREPORTABLE. > This means that I discard these runs from the calculations ? > -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand n.holford
Received on Fri Jul 20 2007 - 21:04:58 EDT

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