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RE: distribution assumption of Eta in NONMEM

From: Ribbing, Jakob <Jakob.Ribbing>
Date: Tue, 1 Jun 2010 17:50:27 +0100

Dear Ethan,


I am not 100% on exactly who said what in this thread and do not want to
put my words in anyone else's mouth, but I think we can all agree on

* In simulation mode NONMEM assumes a normal distribution of etas
* In estimation mode omega can be seen as an estimate of the
variance of eta's. Depending on the parameterisation of random effects
(e.g. additive/proportional vs. log normal) and estimation method;
estimates of population parameters may become biased or imprecise if the
true eta distribution is not close to normal. One clear indication of
this problem is if we have very little shrinkage and the EBE eta
distribution from a large study is skewed.
* We may often come closer to a normal distribution of etas by
applying a so-called semi-parametric distribution of individual
parameters. If this transformation is providing substantially lower OFV
we can expect that it also improves simulation properties of the model
(and possibly also improves accuracy and precision of population
parameters). In what transformation to use, we may rely on OFV (testing
various transformations), the nonmem nonparametric estimation, or, in
case of rich data the shape of the EBE eta distribution (may give some
hint even with shrinkage).


Consequently, depending on the context, estimation in nonmem may or may
not assume a normal distribution of etas. However, even when normality
is not an assumption of the estimation method it may be desirable to
approach a normal distribution (unless non-parametric estimation), to
improve simulation properties. In my opinion this may be worthwhile even
if I do not see clear benefits in VPC:s etc. I do not want even 0.5% of
patients to have a negative effect of drug treatment
(additive/proportional eta) if I believe that is an impossible outcome.
I do not want 1% of all subjects to have more than 100% enzyme
inhibition, etc.


Best regards




Received on Tue Jun 01 2010 - 12:50:27 EDT

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