rPowerSampleSize (version 1.0.2)

bonferroni.1m.ssc: Sample Size Computation with Single Step Bonferroni Method in the Context of Multiple Continuous Endpoints.

Description

This function computes the sample size for an analysis of multiple test with a single step Bonferroni procedure.

Usage

bonferroni.1m.ssc(mean.diff, sd, cor, power = 0.8, alpha = 0.05,
alternative = "two.sided")

Arguments

mean.diff

vector of the mean differences of the m endpoints between both groups under the alternative hypothesis.

sd

vector of the standard deviations of the m endpoints. These are assumed identical for both groups.

cor

correlation matrix between the endpoints. These are assumed identical for both groups.

power

value which corresponds to the chosen power.

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".

Value

Sample size

The required sample size.

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. Adcock, C. J. (2007). Sample size determination: a review. Journal of the Royal Statistical Society: Series D (The Statistician), 46:261-283.

See Also

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

Examples

# NOT RUN {
# Sample size computation for the global method
bonferroni.1m.ssc(mean.diff = c(0.1, 0.2, 0.3), sd = c(1, 1,1 ), cor =
diag(1, 3))
# }