NONMEM Users Network Archive

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Re: [NMusers] Parameter uncertainty

From: Fanny Gallais <gallais.fanny_at_gmail.com>
Date: Thu, 16 Feb 2017 12:01:16 +0100

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.Williams_at_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-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com]
> *On Behalf Of *Fanny Gallais
> *Sent:* Wednesday, February 15, 2017 2:55 AM
> *To:* nmusers_at_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
>
>
>
>
>
>
>

Received on Thu Feb 16 2017 - 06:01:16 EST

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