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

this:

* 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

Jakob

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

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

this:

* 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

Jakob

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