Creating new datasets for each iteration of a power analysis - r

Question

I have the following code to estimate the power for my study which runs perfectly fine. The issue is that I am running n = 1000 iterations, but each iteration generates the exact same dataset. I think this is because the commands in the function that I created (powercrosssw) draw on the data definitions above that are fixed in value? How do I ensure that each dataset (named dx below) that is generated is different (i.e. the values for u_3, error, and y are different for each iteration) so that I am calculating the power appropriately?
library(simstudy)
library(nlme)
library(gendata)
library(data.table)
library(geepack)
set.seed(12345)
clusterDef <- defDataAdd(varname = "u_3", dist = "normal", formula = 0, variance = 25.77) #cluster-level random effect
patError <- defDataAdd(varname = "error", dist = "normal", formula = 0, variance = 38.35) #error term
#Generate cluster-level data
cohortsw <- genData(3, id = "cluster")
cohortsw <- addColumns(clusterDef, cohortsw)
cohortswTm <- addPeriods(cohortsw, nPeriods = 6, idvars = "cluster", perName = "period")
cohortstep <- trtStepWedge(cohortswTm, "cluster", nWaves = 3, lenWaves = 1, startPer = 1, grpName = "Ijt")
cohortstep
#Generate individual patient-level data
pat <- genCluster(cohortswTm, cLevelVar = "timeID", numIndsVar = 5, level1ID = "id")
pat
dx <- merge(pat[, .(cluster, period, id)], cohortstep, by = c("cluster", "period"))
dx <- addColumns(patError, dx)
setkey(dx, id, cluster, period)
#Define outcome y
outDef <- defDataAdd(varname = "y", formula = "17.87 + 5.0*Ijt - 5.42*I(period == 1) - 5.72*I(period == 2) - 7.03*I(period == 3) - 6.13*I(period == 4) - 9.13*I(period == 5) + u_3 + error", dist = "normal")
dx <- addColumns(outDef, dx)
#Fit GLMM model to simulated dataset
model1 <- lme(y ~ factor(period) + factor(Ijt), random = ~1|cluster, data = dx, method = "REML")
summary(model1)
#Power analysis
powercrosssw <- function(nclus = 3, clsize = 5) {
cohortsw <- genData(nclus, id = "cluster")
cohortsw <- addColumns(clusterDef, cohortsw)
cohortswTm <- addPeriods(cohortsw, nPeriods = 6, idvars = "cluster", perName = "period")
cohortstep <- trtStepWedge(cohortswTm, "cluster", nWaves = 3, lenWaves = 1, startPer = 1, grpName = "Ijt")
pat <- genCluster(cohortswTm, cLevelVar = "timeID", numIndsVar = clsize, level1ID = "id")
dx <- merge(pat[, .(cluster, period, id)], cohortstep, by = c("cluster", "period"))
dx <- addColumns(patError, dx)
setkey(dx, id, cluster, period)
return(dx)
}
bresult <- NULL
presult <- NULL
eresult <- NULL
intercept <- NULL
trt <- NULL
timecoeff1 <- NULL
timecoeff2 <- NULL
timecoeff3 <- NULL
timecoeff4 <- NULL
timecoeff5 <- NULL
ranclus <- NULL
error <- NULL
i=1
while (i < 1000) {
cohortsw <- powercrosssw()
#Fit multi-level model to simulated dataset
model1 <- tryCatch(lme(y ~ factor(period) + factor(Ijt), data = dx, random = ~1|cluster, method = "REML"),
warning = function(w) { "warning" }
)
if (! is.character(model1)) {
coeff <- coef(summary(model1))["factor(Ijt)1", "Value"]
pvalue <- coef(summary(model1))["factor(Ijt)1", "p-value"]
error <- coef(summary(model1))["factor(Ijt)1", "Std.Error"]
bresult <- c(bresult, coeff)
presult <- c(presult, pvalue)
eresult <- c(eresult, error)
i <- i + 1
}
}