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.bergstr...@pharmetheus.com www.pharmetheus.com +46(0)18 513 328 U-A Science Park, Dag Hammarskjölds v. 52b 752 37 Uppsala, Sweden *This communication is confidential and is only intended for the use of the individual or entity to which it is directed. It may contain information that is privileged and exempt from disclosure under applicable law. If you are not the intended recipient please notify us immediately. Please do not copy it or disclose its contents to any other person.* *From:* owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] *On Behalf Of *William Denney *Sent:* Wednesday, February 15, 2017 1:01 PM *To:* Fanny Gallais <gallais.fa...@gmail.com> *Cc:* nmusers@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.fa...@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
