crt.long.cont: Power for test of treatment effect in longitudinal cluster...

In powertools: Power and Sample Size Tools

Power for test of treatment effect in longitudinal cluster randomized trial with baseline measurement

Description

This function computes power and sample size for a cluster randomized trial in which a continuous outcome variable is measured during both baseline and follow-up periods among the cluster members, and it is planned that the outcome data will be analyzed using a linear mixed model in which the dependent variable vector includes both baseline and follow up measurements and there is a random intercept for cluster. This function can solve for power, J1, J.ratio, m or delta.

Usage

crt.long.cont(
  m = NULL,
  J1 = NULL,
  J.ratio = 1,
  delta = NULL,
  sd = 1,
  icc = 0,
  cac = 0,
  sac = 0,
  alpha = 0.05,
  power = NULL,
  sides = 2,
  v = FALSE
)

Arguments

m	The number of subjects measured during each cluster-period.
J1	The number of clusters in arm 1.
J.ratio	The ratio J2/J1 between the number of clusters in the two arms; defaults to 1 (equal clusters per 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.
cac	The cluster autocorrelation; defaults to 0.
sac	The subject autocorrelation; defaults to 0.
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

The intraclass correlation coefficient (icc) is the correlation between two observations from different subjects in the same cluster and same time period. Denote the correlation between observations from two different subjects in the same cluster but different time periods as iccb. The cluster autocorrelation (cac) is iccb/icc and is interpreted as the proportion of the cluster-level variance that is time-invariant. Denote the correlation between two observations from the same subject in different time periods as rhoa. The subject autocorrelation (sac) is (rhoa - icc)/(iccb - icc) and is interpreted as the proportion of the subject-level variance that is time-invariant. The sac is only relevant for design in which the same subjects are measured at both baseline and follow up. If different subjects are measured during different time periods, sac should be set to zero.

Value

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

Examples

crt.long.cont(m = 30, J1 = 8, delta = 0.3, icc = 0.05, cac = 0.4, sac = 0.5)

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