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RE: [NMusers] Parameter uncertainty

From: Leander, Jacob <Jacob.Leander_at_astrazeneca.com>
Date: Thu, 16 Feb 2017 13:16:39 +0000

Hi Fanny, Marc

I was thinking in the same direction as Marc. If you use MCMC (BAYES method in NONMEM) the algorithm will provide you with samples from the posterior density (posterior = likelihood * prior). From these samples you can then investigate different statistics, for example variance of your parameters. Be caution about convergence of the algorithm, since these algorithms are not guaranteed to sample uncorrelated samples.

On the same topic, are there any good comparisons out there comparing the standard covariance matrix approach, bootstrap, profiling and MCMC?

/Jacob
From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On Behalf Of Marc Gastonguay
Sent: den 16 februari 2017 13:23
To: Fanny Gallais <gallais.fanny_at_gmail.com>
Cc: Williams, Jason <Jason.Williams_at_pfizer.com>; nmusers_at_globomaxnm.com
Subject: Re: [NMusers] Parameter uncertainty

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<mailto: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<mailto: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> [mailto: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<mailto: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









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Marc R. Gastonguay, Ph.D.<mailto:marcg_at_metrumrg.com>
CEO
Metrum Research Group LLC<http://metrumrg.com>
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Received on Thu Feb 16 2017 - 08:16:39 EST

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