RE: Parameter uncertainty

From: DJ Eleveld-Ufkes Date: February 15, 2017 technical Source: mail-archive.com
Hi Fanny, Likelihood profiles are very useful to asses parameter uncertainty. I am sure you find a tutorial somewhere how they work. A number of software packages automate the process quite a bit. They are usually much more computationally efficient than bootstrap. Warm regards, Douglas Eleveld
Quoted reply history
From: [email protected] [mailto:[email protected]] On Behalf Of Fanny Gallais Sent: woensdag 15 februari 2017 11:55 To: [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 ________________________________
Feb 15, 2017 Fanny Gallais Parameter uncertainty
Feb 15, 2017 DJ Eleveld-Ufkes RE: Parameter uncertainty
Feb 15, 2017 Max Taubert RE: Parameter uncertainty
Feb 15, 2017 William Denney Re: Parameter uncertainty
Feb 15, 2017 Pieter Colin RE: Parameter uncertainty
Feb 15, 2017 Martin Bergstrand RE: Parameter uncertainty
Feb 15, 2017 Leonid Gibiansky Re: FW: Parameter uncertainty
Feb 15, 2017 Jason Williams RE: Parameter uncertainty
Feb 16, 2017 Fanny Gallais Re: Parameter uncertainty
Feb 16, 2017 Marc Gastonguay Re: Parameter uncertainty
Feb 16, 2017 Jacob Leander RE: Parameter uncertainty