Hi Mark,
If you really have 10,000 fold differences in, say, volume or
bioavailability, population model does not make any sense: individual
parameters have uninformative priors; they are defined by the individual
data only, no meaningful predictions can be made for the next patient.
So, if you need data description, you can directly see whether the
method provides you with the correct line, but you cannot count on
prediction: they can be anywhere.
For the estimation procedure, my understanding is that large OMEGAs will
discount population model influence on the individual fit, and in this
respect, the method will give you the correct answer (individual
parameters controlled by the individual data only). This is how you
trick nonmem into the individual model fit: assign huge OMEGAs. Whether
your true OMEGA value is 50 or 150 is more or less irrelevant: both
values are huge and do not provide informative priors for the individual
parameters.
Sometimes you get huge OMEGAs if there is a strong correlation between
parameters, so that combination of ETAs is finite while each of them
individually can be anywhere. Removal of some random effects can help in
this case. Sometimes large OMEGAs are indicative of multivariate
distributions (or strong categorical covariate effects): this will be
seen on ETA distributions histograms or ETAs vs covariates plots.
Overall, I think you have problems with the model or data rather than
with the estimation method failure.
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:
General question:
What are practical limits on the magnitude of OMEGA that is compatible
with the FO and FOCE/I method? I seem to recall Stuart at one time
suggesting that a CV of 0.5 (exponential OMEGA of 0.5) was about the
limit at which the Taylor expansion can be considered a reasonable
approximation of the real distribution. What about FOCE-I?
I'm asking because I have a model that has an OMEGA of 13, exponential
(and sometime 100) FOCE-I, and it seems to be very poorly behaved in
spite of overall, reasoable looking data (i.e., the structural model
traces a line that looks like the data, but some people are WAY above
the line and some are WAY below, and some rise MUCH faster, and some
rise MUCH later, by way I mean >10,000 fold, but residual error looks
not too bad). Looking at the raw data, I believe that the the
variability is at least this large. Can I beleive that NONMEM FOCE
(FO?) will behave reasonably?
thanks
Mark
