twoarm_sim {crctStepdown} | R Documentation |
Simple simulation of two Poisson distributed outcomes for a two-arm parallel cluster randomised trial with no baseline measures. A log-linear model is specified y~Poisson(lambda) with lambda = exp(mu + beta*D + theta) where D is the treatment effect indicator equal to one in clusters with the treatment and zero otherwise, and theta~N(0,sigma^2) is the cluster random effect. Used for testing error rates of the methods.
twoarm_sim(
nJ = c(7, 7),
N = 20,
mu = rep(1, 2),
beta = c(0, 0),
sig_cl = rep(0.05, 2)
)
nJ |
Vector of two integers with the number of clusters in treatment and control arms |
N |
Number of individuals per cluster |
mu |
Vector of two numeric values with the intercept terms for the two models on the log scale |
beta |
Vector of two numeric values that are the treatment effect parameters in the two models |
sig_cl |
Vector of two values equal to the variance of the random effect in each model |
A list consisting of: (1) data frame with the cluster IDs (cl), treatment effect indicators (treat), and two outcomes (y1, y2), and (2) the values of the treatment effect parameters used in the simulation.
out <- twoarm_sim()
data <- out[[1]]