Description Usage Arguments Value Author(s) References See Also Examples

This function aims at analysing *m* multiple continuous endpoints with a global procedure. The clinical aim
is to be able to detect a mean difference between the test *T* and
the control *C* product for at least one endpoint among *m*. This method is based on a multivariate model taking into account
the correlations between the *m* endpoints and possibly some adjustment variables. The result gives only a global decision.

1 | ```
global.1m.analysis(XC, XT, A, alpha = 0.05, n = NULL)
``` |

`XC` |
matrix of the outcome for the control group. |

`XT` |
matrix of the outcome for the test group. |

`A` |
matrix of the adjustment variables. |

`n` |
sample size of a group. The sample size needs to be the same for each group. |

`alpha` |
value which corresponds to the chosen Type-I error rate bound. |

`Pvalue` |
the p-value of the global test. |

P. Lafaye de Micheaux, B. Liquet and J. Riou

Lafaye de Micheaux P., Liquet B., Marque S., Riou J. (2014). Power and
Sample Size Determination in Clinical Trials With Multiple Primary
Continuous Correlated Endpoints, *Journal of
Biopharmaceutical Statistics*, 24, 378–397.

`global.1m.ssc`

,
`indiv.1m.ssc`

,
`indiv.1m.analysis`

,
`bonferroni.1m.ssc`

1 2 3 4 5 6 7 8 9 10 |

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