# crtpwr.2prop: Power calculations for simple cluster randomized trials,... In clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials

## Description

Compute the power of a simple cluster randomized trial with a binary outcome, or determine parameters to obtain a target power.

## Usage

 ```1 2 3``` ```crtpwr.2prop(alpha = 0.05, power = 0.8, m = NA, n = NA, cv = 0, p1 = NA, p2 = NA, icc = NA, pooled = FALSE, p1inc = TRUE, 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. `m` The number of clusters per condition. It must be greater than 1. `n` The mean of the cluster sizes. `cv` The coefficient of variation of the cluster sizes. When `cv` = 0, the clusters all have the same size. `p1` The expected proportion in the treatment group. `p2` The proportion in the control group. `icc` The intraclass correlation. `pooled` Logical indicating if pooled standard error should be used. `p1inc` Logical indicating if p1 is expected to be greater than p2. `tol` Numerical tolerance used in root finding. The default provides at least four significant digits.

## Value

The computed argument. #' @examples # Find the number of clusters per condition needed for a trial with alpha = .05, # power = 0.8, 10 observations per cluster, no variation in cluster size, probability in condition 1 of .1 and condition 2 of .2, and icc = 0.1. crtpwr.2prop(n=10 ,p1=.1, p2=.2, icc=.1) # # The result, showimg m of greater than 37, suggests 38 clusters per condition should be used.

## Authors

Jonathan Moyer ([email protected])

clusterPower documentation built on Sept. 5, 2017, 9:06 a.m.