Hi Fanny, As I understand it, you’re looking for ways to produce predictions according to your model taking into account parameter uncertainty. We’ve recently published on the importance of parameter uncertainty when considering probability of target attainment for antibiotic dosing regimens. (Colin et al. J Antimicrob Chemother (2016) 71 (9): 2502-2508)
The online supplement to this paper holds an R-script which you can use to simulate (and calculate PTA, if relevant) taking into account parameter uncertainty. For this, the script uses the variance-covariance matrix that is produced by the $COV step in NONMEM. Of course other techniques which generate a var-cov matrix could be used as input for the script as well. Kind regards, Pieter Colin 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
