From: Leonid Gibiansky <*LGibiansky*>

Date: Sat, 19 Jul 2008 17:36:56 -0400

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
*

*> *

Received on Sat Jul 19 2008 - 17:36:56 EDT

Date: Sat, 19 Jul 2008 17:36:56 -0400

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:

Received on Sat Jul 19 2008 - 17:36:56 EDT