[R] calculate power-linear mixed effect model
bgunter@4567 @end|ng |rom gm@||@com
Fri Sep 17 22:05:52 CEST 2021
Wrong list! Post on r-sig-mixed-models, not here.
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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:
> But I am not sure how to adapt the code given in the tutorial so that
> it is matching to mine design.
> Can you please help,
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