crt.parallel.bin: Power calculation for cluster randomized trial with binary...

In powertools: Power and Sample Size Tools

Power calculation for cluster randomized trial with binary outcome

Description

This function performs power and sample size calculations for a two-arm cluster randomized trial with a binary outcome. It assumes the outcome analysis will be conducted using a mixed effect logistic regression model that has a random intercept for cluster. Equal allocation of clusters to arms is assumed. Can solve for power, J, m or alpha.

Usage

crt.parallel.bin(
  m = NULL,
  m.sd = 0,
  J = NULL,
  pc = NULL,
  pt = NULL,
  sigma.u = NULL,
  alpha = 0.05,
  power = NULL,
  sides = 2,
  v = FALSE
)

Arguments

m	The number of subjects per cluster.
m.sd	The standard deviation of cluster sizes (provide if unequal number of participants per cluster); defaults to 0.
J	The total number of clusters (over both arms).
pc	The probability of the outcome in control clusters.
pt	The probability of the outcome in treatment clusters.
sigma.u	Standard deviation of the cluster random effect (random intercept).
alpha	The significance level (type 1 error rate); defaults to 0.05.
power	The specified level of power.
sides	Either 1 or 2 (default) to specify a one- or two- sided hypothesis test.
v	Either TRUE for verbose output or FALSE (default) to output computed argument only.

Details

For help selecting a reasonable value for sigma.u, consider using the crt.varexplore function.

Value

A list of the arguments (including the computed one).

Examples

crt.parallel.bin(m = 60, J = NULL, pc = 0.25, pt = 0.15, sigma.u = 0.3, power = 0.8)
crt.parallel.bin(m = 60, m.sd = 1, J = NULL, pc = 0.25, pt = 0.15, sigma.u = 0.3, power = 0.8)

powertools documentation built on April 4, 2025, 5:02 a.m.