RE: Parameter uncertainty
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
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Marc Gastonguay
Sent: den 16 februari 2017 13:23
To: Fanny Gallais <[email protected]>
Cc: Williams, Jason <[email protected]>; [email protected]
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
<[email protected]<mailto:[email protected]>> 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
<[email protected]<mailto:[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]>
[mailto:[email protected]<mailto:[email protected]>] On
Behalf Of Fanny Gallais
Sent: Wednesday, February 15, 2017 2:55 AM
To: [email protected]<mailto:[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
--
Marc R. Gastonguay, Ph.D.<mailto:[email protected]>
CEO
Metrum Research Group 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
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