Dear Fanny, Another useful tool you may want to try is using the mrgsolve package available in R, developed by Kyle Baron at Metrum Research Group. I have found mrgsolve to be very efficient for PKPD simulation and sensitivity analysis in R. There is an example of incorporating parameter uncertainty (from $COV step in NONMEM) in Section 9 of the example on Probability of Technical Success (link below).
https://github.com/mrgsolve/examples/blob/master/PrTS/pts.pdf Best regards, Jason From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Fanny Gallais Sent: Wednesday, February 15, 2017 2:55 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
