Thank you all for your responses. It is going to be very useful for my work.
Best regards, F.G. 2017-02-15 17:35 GMT+01:00 Williams, Jason <jason.willi...@pfizer.com>: > 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 > > > > > > >
