Description Usage Arguments Value Authors

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

1 2 3 |

`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 |

`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. |

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.

Jonathan Moyer ([email protected])

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

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