twoarm_sim {crctStepdown}

Simulates data from a two-arm parallel cluster randomised trial

Description

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.

Usage

twoarm_sim(
  nJ = c(7, 7),
  N = 20,
  mu = rep(1, 2),
  beta = c(0, 0),
  sig_cl = rep(0.05, 2)
)

Arguments

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

Value

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.

Examples

out <- twoarm_sim()
data <- out[[1]]