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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Smith, Mike K Sent: Monday, November 26, 2007 9:56 AM To: [email protected] 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 LEGAL NOTICE Unless expressly stated otherwise, this message is confidential and may be privileged. It is intended for the addressee(s) only. Access to this e-mail by anyone else is unauthorised. If you are not an addressee, any disclosure or copying of the contents of this e-mail or any action taken (or not taken) in reliance on it is unauthorised and may be unlawful. If you are not an addressee, please inform the sender immediately. Pfizer Limited is registered in England under No. 526209 with its registered office at Ramsgate Road, Sandwich, Kent CT13 9NJ
