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RE: [NMusers] Parameter uncertainty

From: Martin Bergstrand <martin.bergstrand_at_pharmetheus.com>
Date: Wed, 15 Feb 2017 14:06:23 +0100

Dear Fanny and Bill,



The sampling importance resampling (SIR) approach [1] to characterize the
parameter uncertainty address the aspects pointed out Bill. In my opinion
this is currently in general the most widely applicable and accurate method
to characterize parameter uncertainty for NLMEM (bootstrap is likely
approximately as good for large datasets and balanced designs). The method
is implemented in PsN [2] and ready to use together with NONMEM.



[1] Dosne A-G, Bergstrand M, Harling K, Karlsson MO. Improving the
estimation of parameter uncertainty distributions in nonlinear mixed
effects models using sampling importance resampling. J Pharmacokinet
Pharmacodyn. 2016 Oct 11.

http://link.springer.com/article/10.1007/s10928-016-9487-8

[2] SIR user guide, PsN 4.6.0:
http://psn.sourceforge.net/pdfdocs/sir_userguide.pdf



Best regards,



Martin Bergstrand, Ph.D.

Senior Consultant

Pharmetheus AB



+46(0)709 994 396

martin.bergstrand_at_pharmetheus.com

www.pharmetheus.com



+46(0)18 513 328

U-A Science Park, Dag Hammarskjölds v. 52b

752 37 Uppsala, Sweden



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*From:* owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] =
*On
Behalf Of *William Denney
*Sent:* Wednesday, February 15, 2017 1:01 PM
*To:* Fanny Gallais <gallais.fanny_at_gmail.com>
*Cc:* nmusers_at_globomaxnm.com
*Subject:* Re: [NMusers] Parameter uncertainty



Hi Fanny,



It is often good practice to fit parameters that must be positive on the
log scale (by exponentiating them). That will ensure that when sampling
from a normal distribution (and then exponentiating the sample) you will
have a positive value.



LLP was suggested, but it won't assess correlation between your parameters
which is often important when running simulations.



Bootstrap is another good alternative as has already been suggested.



Thanks,



Bill


On Feb 15, 2017, at 5:55 AM, Fanny Gallais <gallais.fanny_at_gmail.com> wrote:

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 Wed Feb 15 2017 - 08:06:23 EST

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