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Re: Models that abort before convergence

From: Nick Holford <n.holford>
Date: Fri, 21 Nov 2008 15:10:12 +1300


[Although Leonid originally wrote personallyy to me he has kindly
allowed me to copy his comments and add mine for nmusers to read]

You gave me an anecdote so let my respond with mine. My first NONMEM
project involved a data set I use today for beginners courses in NONMEM.

If you use a very naive model and FO then NONMEM reports success and
completes the covariance step. If you use FOCE and the model that Lewis
Sheiner helped me develop (Holford et al. 1993; see URL above for the
code) then the run finishes with rounding errors and no covariance.
[This is compiler dependent which is just another thing to be aware of].

So if I was your student without a good mentor I might have concluded
from the first run that I had a good model when in fact the model was
really very poor. This is the lesson I want to teach. Never trust
NONMEM's successful minimization and covariance to imagine you have a
good model.

Your examples are a priori bad models (as you yourself describe them)
without even looking at the data. My example is based on real data. One
cannot know if the model is good or bad without more insight and
meaningful diagnostics. In those days (1989) we didn't have VPC or NPDE
but we did think about what we understood about the disease and the
drug. We got past the simple "covariance step ran" stopping point and
went on to explore a drug and a disease that became the model for the
central example for the learning and confirming philosophy (Sheiner 1997).

Best wishes,


Holford NHG, Hashimoto Y, Sheiner LB. Time and theophylline
concentration help explain the recovery of peak flow following acute
airways obstruction. Clin Pharmacokinet. 1993;25(6):506-15.
Sheiner LB. Learning versus confirming in clinical drug development.
Clinical Pharmacology & Therapeutics. 1997;61(3):275-91.

> Hi Nick,
> It is nice to speak with you even via e-mail
> Just an example:
> I had a perfect model, no problem with the convergence and cov steps with
> Now, I tried
> You may say that it is stupid, but this is an extreme approximation of
> with all subjects being SEX=1
> I got an error message that $COV failed.
> Should I ignore it and move on as you suggest or look for the reasons?
> Then, I moved to
> Even more stupid? But this is an approximation of the situation where I
> have BOV for study with just 1 occasion.
> Now I have rounding errors.
> If I ignore the diagnostic, I will not recover these problems from the
> or DV vs PRED, or OF value. In order to understand the problem we need to
> study the model, but we are prompted by the error message to do so in
> more
> details.
> We also need to be careful whom we talk to. I was told many times that
> our
> nmgroup posts are used to teach students, and there are a lot of junior
> guys who learn by their own. I think, your extreme views ( :) ) convey
> the
> wrong messages to this group of people. While I fully agree that you
> personally can choose to ignore the error messages and still find the
> good
> model, I would not advice a person who has limited nonmem experience to
> ignore the program output.
> Best wishes
> Leonid

Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
Received on Thu Nov 20 2008 - 21:10:12 EST

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