cpa.did.normal: Power calculations for difference-in-difference cluster...
In clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials

Description Usage Arguments Details Value Note Authors References Examples

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

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
)

`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.

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.

The computed argument.

This function was inspired by work from Stephane Champely (pwr.t.test) and Peter Dalgaard (power.t.test). As with those functions, 'uniroot' is used to solve power equation for unknowns, so you may see errors from it, notably about inability to bracket the root when invalid arguments are given.

Jonathan Moyer (jon.moyer@gmail.com), Ken Kleinman (ken.kleinman@gmail.com)

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

# 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.