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RE: Models that abort before convergence

From: LGibiansky
Date: Tue, 18 Nov 2008 23:13:43 -0500

I do not support extreme views (from places where people walk upside down
:) ) that Nonmem error messages should be ignored: they serve the useful
purpose to alert when Nonmem is having some difficulties, and should alway=
be part of the picture. If the data looks good, model is simple, then we=

need to look for the reason for the poor convergence. Sometimes it helps=
use SIGDIG= 5 or 6 to get 3 significant digits precision. But if you a=
working on the limit of the algorithms (as implemented) abilities:
nonlinear model + stiff differential equations + large range of doses and
concentrations, etc., then you face the situation when you cannot force
convergence even if you try hard. On my recent project, none of the
intermediate model converged even though bootstrap provided pretty narrow
CI (so it does not look like over-parametrized model), all diagnostic plot=
were good, and the visual predictive check was reasonable. Then you just=

blame the algorithm and move on. You loose the ability to justify your
covariate selection based on the objective function drop (which is not a
good idea any way), and may need to provide a little bit more detailed
investigation to convince reviewers (regulatory and/or journal) that the
model is adequate for the intended purpose.

Original Message:
From: Dennis Fisher fisher
Date: Tue, 18 Nov 2008 11:21:23 -0800
To: nmusers
Subject: [NMusers] Models that abort before convergence


I am curious as to your thoughts about a particular NONMEM issue. I =

often find myself in a situation where a complex model does not
converge to 3 digits ("no of digits: unreportable") yet the objective
function is markedly better than a previous model and graphics suggest =

that the model is quite good (and better than the previous one). Nick =

Holford has advocated (and I agree) that NONMEM's SE's have minimal
utility and the inability to calculate them is not important.
However, I have not seen similar discussion about whether one can /
should accept a model that did not converge.

The particular situation that I dealing with at the moment is that a
dataset that I am analyzing yielded a series of results that did not
converge as I added parameters (despite an improving fit and a marked
decrease in the objective function), then yet a more complicated model =

yielded 3.0 significant digits. In this case, there is no problem (I =
can use this final model for bootstrap, VPC, etc.) but what if none of =

these models had converged.


Dennis Fisher MD
P < (The "P Less Than" Company)
Phone: 1-866-PLessThan (1-866-753-7784)
Fax: 1-415-564-2220

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Received on Tue Nov 18 2008 - 23:13:43 EST

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