RE: Parameter uncertainty
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
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Fanny Gallais
Sent: Wednesday, February 15, 2017 2:55 AM
To: [email protected]
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