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calculate power-linear mixed effect model

2 messages · Ana Marija

Original
Ana Marija
# 
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.

Can you please help,

Thanks
Ana
Ana Marija
# 
I tried doing this but I am unsure if I am on the right track:

library(simr)

## trying to simulate some data and run a power calculation
x1 <- rnorm(1172) ## creating a continuous variable
x2 <- sample(1:2,1172,replace=T) # creating some sort of grouping
variable with 2 groups
y <- rbinom(n = 1172, size = 1, prob= 0.3) # creating a binary (0,1)
response variable (with probability of success = 0.3)
a=age(1172, x = 18:89, prob = NULL, name = "Age")   #simulating age
with ?wakefield? package
s=sex(1172)   #simulating sex with ?wakefield? package
df <- data.frame(y = y, x1 = x1, x2 = x2, a=a, s=s) #merging into one data set
y          x1 x2  a      s
1 0 -0.28876179  1 53   Male
2 0 -0.05696877  2 23 Female

m1 <- lmer(y ~ x1+a+s + (1|x2), data = df)

fixef(m1)["x1"] <-0.337

powerSim(m1)

I am getting a bunch of these messages when I run this:

?boundary (singular) fit: see ?isSingular?
On Fri, Sep 17, 2021 at 3:10 PM Ana Marija <sokovic.anamarija at gmail.com> wrote: