Re: Parameter uncertainty
Thank you all for your responses. It is going to be very useful for my
work.
Best regards,
F.G.
Quoted reply history
2017-02-15 17:35 GMT+01:00 Williams, Jason <[email protected]>:
> 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:* [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
>
>
>
>
>
>
>