clusterPower (version 0.7.0)

cpa.did.normal: Power calculations for difference-in-difference cluster randomized trials, continuous outcome

Description

Compute the power of a difference-in-difference cluster randomized trial design with a continuous outcome, or determine parameters to obtain a target power.

Usage

cpa.did.normal(
  alpha = 0.05,
  power = 0.8,
  nclusters = NA,
  nsubjects = NA,
  d = NA,
  ICC = NA,
  rho_c = NA,
  rho_s = NA,
  vart = NA,
  tol = .Machine$double.eps^0.25
)

Arguments

alpha

The level of significance of the test, the probability of a Type I error.

power

The power of the test, 1 minus the probability of a Type II error.

nclusters

The number of clusters per condition. It must be greater than 1.

nsubjects

The mean of the cluster sizes, or a vector of cluster sizes for one arm.

d

The difference in mean change between conditions (i.e. "difference-in-difference").

ICC

The intraclass correlation.

rho_c

The correlation between baseline and post-test outcomes at the cluster level. This value can be used in both cross-sectional and cohort designs. If this quantity is unknown, a value of 0 is a conservative estimate.

rho_s

The correlation between baseline and post-test outcomes at the subject level. This should be used for a cohort design or a mixture of cohort and cross-sectional designs. In a purely cross-sectional design (baseline subjects are completely different from post-test subjects), this value should be 0.

vart

The total variation of the outcome (the sum of within- and between-cluster variation).

tol

Numerical tolerance used in root finding. The default provides at least four significant digits.

Value

The computed argument.

Authors

Jonathan Moyer (), Ken Kleinman ()

Details

Exactly one of alpha, power, nclusters, nsubjects, d, ICC, rho_c, rho_s, and vart must be passed as NA. Note that alpha andpower have non-NA defaults, so if those are the parameters of interest they must be explicitly passed as NA.

If nsubjects is a vector the values, nclusters will be recalculated using the values in nsubjects.

References

Rutterford C, Copas A, Eldridge S. (2015) Methods for sample size determination in cluster randomized trials. Int J Epidemiol. 44(3):1051-1067.

Teerenstra S, Eldridge S, Graff M, de Hoop E, Borm, GF. (2012) A simple sample size formula for analysis of covariance in cluster randomized trials. Statist Med. 31:2169-2178

Examples

# NOT RUN {
# Find the number of clusters per condition needed for a trial with alpha = 0.05, 
# power = 0.80, nsubjects = 100, d = 0.50 units, ICC = 0.05, rho_c = 0.50, rho_s = 0.70,
# and vart = 1 unit.
cpa.did.normal(nsubjects = 100 , d = 0.5, ICC = 0.05, rho_c = 0.50, rho_s = 0.70, vart = 1)
# 
# The result, nclusters = 4.683358, suggests 5 clusters per condition should be used.

# }