[NMusers] Simulation for future populations and diagnostics

Dear NMusers,
I had to recently run an exercise with simulations across uncertainty in =
not just THETA, but also OMEGA and SIGMA. I simply parameterized the =
omegas and sigmas as thetas (many thanks to Dr. Gibiansky's posting on =
NMusers on how to implement this with omegas and sigmas fixed to one). =
With this trick all parameters were reported out as thetas and then I =
simply used rmvnorm and the covariance matrix from NONMEM to then =
accomplish the task. The results looked reasonable and I was wondering =
if anybody has any experience with this trick to answer the uncertainty =
question.
 
Hoping to get feedback...Mahesh

PS. The long-drawn-out way to do this could also be to use results from =
bootstrap replicates (e.g. nmbs with WFN) to simulate across variability =
and uncertainty. This is sometimes not very practical if the bootstrap =
run takes days (or weeks) to run. See PAGE poster for implementation: =
http://www.page-meeting.org/?abstract=1220

-----Original Message-----
From: owner-nmusers_at_globomaxnm.com =
[mailto:owner-nmusers_at_globomaxnm.com]On Behalf Of Smith, Mike K
Sent: Monday, November 26, 2007 9:56 AM
To: nmusers_at_globomaxnm.com
Subject: [NMusers] Simulation for future populations and diagnostics



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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Received on Mon Nov 26 2007 - 12:51:59 EST