Re: Error Message from NONMEM :: MINIMIZATION TERMINATED (ERROR=136)

From:Nick Holford Subject:Re: [NMusers] Error Message from NONMEM :: MINIMIZATION TERMINATED (ERROR=136) Date:Thu, 19 Sep 2002 09:05:45 +1200 Partha, The standard (and IMHO usually unhelpful) suggestion it to check the structure of your model for errors. This may also involve considering posteriori non-identifiability because the data does not really let you estimate some parameter such as between subject variability. This leads to a KISS (Keep It Simple Stupid) approach to model building that may throw the baby out with the bathwater. I find that this typically happens when the model building is getting really interesting and I am learning something new about the system I am trying to describe. I judge the usefulness of the model (remembering George Box) by its ability to describe the data rather than some arbitrary numerical criterion such as significant digits e.g. I am currently working on a model describing the placebo response in depression. Only the simplest model converges and runs the covariance step. More complex and informative models visibly fit the data better when I look at time courses of HAMD score but typically fail to minimize (although I can sometimes get at least 3 sig digs by using SIGDIG=4 on $ESTIMATION). I rationalize this by saying that learning via modelling happens at the bleeding edge of the data. We are trying to discover weak but potentially important signals (like the Hubble telescope recent discovery of medium sized black holes http://oposite.stsci.edu/pubinfo/pr/2002/18/) that are buried in the data. Confirming the obvious stuff (like sun, moon, planets) is visible without NONMEM -- that's what statisticians do in their analyses. It is therefore no surprise that the criteria that give statisticians a warm and fuzzy feeling (like asymptotic SEs) are not always discernible (or believable) when trying to extract meaning from experiments not expicitly designed to discover new things. In the particular example you show below the ETABAR estimates do seem to be pathologically different from zero so trying a different between subject variability model may help e.g. use (1+ETA) instead of exp(ETA) if it is possible that the parameter can have different signs in different individuals. Nick Nick Holford, Divn Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/ ___________________________________