# [R] calculate power-linear mixed effect model

Ana Marija @okov|c@@n@m@r|j@ @end|ng |rom gm@||@com
Fri Sep 17 22:10:05 CEST 2021

```Thank you so much for that info!

On Fri, Sep 17, 2021 at 3:06 PM Bert Gunter <bgunter.4567 using gmail.com> wrote:
>
> Wrong list! Post on r-sig-mixed-models, not here.
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
> On Fri, Sep 17, 2021 at 12:22 PM Ana Marija <sokovic.anamarija using gmail.com> wrote:
> >
> > Hi All,
> >
> > I plan to identify metabolite levels that differ between individuals
> > with various retinopathy outcomes (DR or noDR). I plan to model
> > metabolite levels using linear mixed models ref as implemented in
> > lmm2met software. The model covariates will include: age, sex, SV1,
> > SV, and disease_condition.
> >
> > The random effect is subject variation (ID)
> >
> > Disease condition is the fixed effect because I am interested in
> > metabolite differences between those disease conditions.
> >
> > This command  will build a model for each metabolite:
> > fitMet = fitLmm(fix=c('Sex','Age','SV1,'SV2','disease_condition'),
> > random='(1|ID)', data=df, start=10)
> >
> > SV1 and SV2 are surrogate variables (numerical values)
> >
> > Next I need to calculate the power of my study. Let's say that I have
> > 1,172 individuals total in the study, from which 431 are DR. Let's say
> > that I would like to determine the power of this study given the
> > effect size of 0.337.
> >
> > I know about SIMR software in R but I am not sure how to apply it to
> > my study design.
> >
> > I looked at this paper:
> > https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504
> >
> > But I am not sure how to adapt the code given in the tutorial so that
> > it is matching to mine design.
> >
> >
> > Thanks
> > Ana
> >
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