rPowerSampleSize (version 1.0.2)

indiv.1m.analysis: Data analysis with an individual testing procedure in the context of multiple continuous endpoints

Description

This function aims at analysing some multiple continuous endpoints with an individual testing procedure. This method, based on Union-Intersection test procedure, allows one to take into account the correlations between the different endpoints in the analysis.

Usage

indiv.1m.analysis(method, XC, XT, varX = NULL, alpha = 0.05,
alternative = "two.sided", n = NULL)

Arguments

method

description of the covariance matrix estimation. Two choices are possible: "Unknown" (normality assumption and unknown covariance matrix) and "Asympt" (asymptotic context).

XC

matrix of the outcomes for the control group.

XT

matrix of the outcomes for the test group.

varX

covariance matrix. Should be provided when 'method' = 'Known'.

alpha

value which correponds to the chosen Type-I error rate bound.

alternative

character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

n

NULL. Sample size of a group, computed from XC.

Value

UnAdjPvalue

unadjusted p-values.

AdjPvalue

corrected p-values.

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

Examples

# NOT RUN {
# Calling the data
data(data.sim)

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

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

indiv.1m.analysis(method = "UnKnown", XC = XC, XT = XT)
# }