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Re: [NMusers] Parameter uncertainty

From: Marc Gastonguay <marcg_at_metrumrg.com>
Date: Thu, 16 Feb 2017 07:22:43 -0500

Dear Fanny,

One additional method to obtain the parameter uncertainty, which I don't
believe was mentioned, is Bayesian estimation using Markov-Chain Monte
Carlo (MCMC) simulation. This method provides a full joint posterior
distribution (e.g. uncertainty distribution) of the parameters and any
predicted quantities, and is really the gold standard for this type of
goal. It is possible to implement this method in NONMEM (with some
limitations on the prior distributions), or you could use BUGS or Stan with
associated PK model libraries. You can also extract the samples from the
posterior distribution and simulate using the methods already described in
this thread.

Marc

On Thu, Feb 16, 2017 at 6:01 AM, Fanny Gallais <gallais.fanny_at_gmail.com>
wrote:

> 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
>>
>>
>>
>>
>>
>>
>>
>
>


--
Marc R. Gastonguay, Ph.D. <marcg_at_metrumrg.com>
CEO
Metrum Research Group LLC <http://metrumrg.com>
2 Tunxis Rd., Ste 112, Tariffville, CT 06081 USA
Tel: +1.860.735.7043 ext. 101, Mobile: +1.860.670.0744, Fax: +1.860.760.6014

Received on Thu Feb 16 2017 - 07:22:43 EST

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