One of the tools available for simulations is Metrum R package
metrumrg
install.packages("metrumrg", repos="http://R-Forge.R-project.org";)
Example of applications can be found here:
http://www.page-meeting.org/page/page2006/P2006III_11.pdf
Since the time it was written (2005-2006), Nonmem enhanced the
simulations options, so now you can simulate from the model-estimated
uncertainty directly from Nonmem. The R package could be useful if you
do it from the bootstrap results.
Leonid
*From:*owner-nmus...@globomaxnm.com
[mailto:owner-nmus...@globomaxnm.com] *On Behalf Of *Fanny Gallais
*Sent:* Wednesday, February 15, 2017 5:55 AM
*To:* nmusers@globomaxnm.com
*Subject:* [NMusers] Parameter uncertainty
Dear NM users,
I would like to perform a simulation (on R) incorporating parameter
uncertainty. For now I'm working on a simple PK model. Parameters were
estimated with NONMEM. I'm trying to figure out what is the best way to
assess parameter uncertainty. I've read about using the standard errors
reported by NONMEM and assume a normal distribution. The main problem is
this can lead to negative values. Another approach would be a more
computational non-parametric method like bootstrap. Do you know other
methods to assess parameter uncertainty?
Best regards
F. Gallais
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