crt.xo.cont: Power for test of treatment effect in 2x2 crossover cluster...

In powertools: Power and Sample Size Tools

Power for test of treatment effect in 2x2 crossover cluster randomized trial

Description

Power and sample size calculation for a 2x2 crossover cluster randomized trial. Can solve for power, number of clusters per arm (assumes equal number of cluster per arm), m, delta or alpha.

Usage

crt.xo.cont(
  m = NULL,
  J.arm = NULL,
  delta = NULL,
  sd = 1,
  icc = 0,
  icca = 0,
  iccb = NULL,
  cac = NULL,
  sac = 0,
  alpha = 0.05,
  power = NULL,
  sides = 2,
  v = FALSE
)

Arguments

m	The number of subjects measured during each cluster-period.
J.arm	The number of clusters in each arm.
delta	The difference between the intervention and control means under the alternative minus the difference under the null hypothesis.
sd	The total standard deviation of the outcome variable; defaults to 1.
icc	The within-cluster, within-period intraclass correlation coefficient; defaults to 0.
icca	The within-cluster, within-subject correlation (correlation between two measurements within the same subject); defaults to 0.
iccb	The within-cluster, between-period intraclass correlation coefficient. Either iccb OR cac must be specified.
cac	The cluster autocorrelation. Either iccb OR cac must be specified.
sac	The subject autocorrelation; defaults to 0.
alpha	The significance level or 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 to output computed argument only.

Value

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

Examples

crt.xo.cont(m = 30, J.arm = 4, delta = 0.3, icc = 0.05, cac = 0.8, sac = 0.4)
crt.xo.cont(m = 30, J.arm = 4, delta = 0.3, icc = 0.05, icca = 0.42, iccb = 0.04)
crt.xo.cont(m = 30, J.arm = 4, delta = 0.3, icc = 0.05, cac = 0.5)

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