If we're simulating data for a future population (= new trial or new as
yet unstudied population) then am I right in thinking that in order to
"do the correct thing" we should really be simulating across uncertainty
in not just THETA, but also OMEGA and SIGMA?  This would be my
understanding of what happens in fully Bayesian prediction, integrating
out over the current posterior of *all* model parameters.  My
understanding is that this isn't always done when simulating new data.
We often simulate taking into consideration uncertainty in THETA
(sampling from Multivariate Normal) but ignore uncertainty in OMEGA.  I
suppose that one could argue that if we have data for a large number of
subjects who are "exchangeable" with the subjects we are simulating for
then this doesn't matter much.  But in other cases this may be
important.  One difficulty (as mentioned previously on the this list) is
the problem of specifying the appropriate inverse-Wishart distribution
for the OMEGA matrix and then simulating from it. 

In simulating data for the current population (= model diagnostics) I
don't think you need to acknowledge uncertainty in OMEGA, unless you're
doing full PPCs.  Does this sound right?  In that case the population
you are describing is the data you have...  Again, it would be useful to
know what people currently *do* as well as what is "the correct thing".

If anybody has useful references on this topic I would really appreciate
it.  I have spotted and downloaded Leonid Gibiansky and Marc
Gastonguay's poster on the R/NONMEM Toolbox from PAGE, but haven't found
much else. 

Cheers,
Mike

Mike K. Smith
Pharmacometrics

PGRD, Sandwich 
Location: 509/1.117 (IPC 096) 
Tel: +44 (0)1304 643561

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