Re: 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
Quoted reply history
> On Feb 15, 2017, at 5:55 AM, Fanny Gallais <[email protected]> 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
>
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