global.1m.analysis {rPowerSampleSize}

Data analysis with a global method in the context of multiple continuous endpoints

Description

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.

Usage

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

Arguments

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.

Value

Pvalue

the p-value of the global test.

Author(s)

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

References

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.

See Also

global.1m.ssc, indiv.1m.ssc, indiv.1m.analysis, bonferroni.1m.ssc

Examples

# Calling the data
data(data.sim)

# Data analysis for the global method
n <- nrow(data) / 2

XC <- data[1:n, 1:3]
XT <- data[(n + 1):(2 * n), 1:3]

global.1m.analysis(XC = XC, XT = XT, A = data[, 5])