RE: [NMusers] Parameter uncertainty

: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Marc Gastonguay Sent: den 16 februari 2017 13:23 To: Fanny Gallais <gallais.fa...@gmail.com> Cc: Williams, Jason <jason.willi...@pfizer.com>; nmusers@globomaxnm.com Subject: Re: [NMusers] Parameter uncer

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

Re: [NMusers] Parameter uncertainty

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 : > Dear Fanny, > > > > Another useful tool you may want to try is using the mrgsolve package > available in R, developed by

RE: [NMusers] Parameter uncertainty

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

RE: [NMusers] Parameter uncertainty

[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 para

RE: [NMusers] Parameter uncertainty

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.

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

RE: [NMusers] Parameter uncertainty

Dear Fanny, I would use either bootstrapping or likelihood profiling, both of them are implemented in PsN ('bootstrap' and 'llp'). Kind regards Max Taubert Von: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com]" im Auftrag von "Fanny Gallais

RE: [NMusers] Parameter uncertainty

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

Re: [NMusers] Parameter Uncertainty and Covariate effects

Dear Sven If you don't assume the covariance between THETA(1) and THETA(2) to be zero but use the estimated covariance value, you do let the data speak. A problem in this respect is that publications never give such values even if it of course is possible. With online access to model code and