Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes the sample size with an individual testing procedure in the context of multiple continuous endpoints. This method, based on the UnionIntersection testing procedure, allows one to take into account the correlation between the different endpoints in the computation of the sample size.
1 2  indiv.1m.ssc(method, ES, cor, power = 0.8, alpha = 0.05, alternative =
"two.sided", tol = 1e04, maxiter = 1000, tol.uniroot = 1e04)

method 
description of the covariance matrix estimation. Two choices are possible: "Unknown" (normality assumption and unknown covariance matrix) and "Asympt" (asymptotic context). 
ES 
vector indicating the values of the effect size. The definition of the effect size is presented in the "Details" section. 
cor 
matrix indicating the correlation matrix between the endpoints. 
power 
value which corresponds to the chosen power. 
alpha 
value which correponds to the chosen TypeI error rate bound. 
alternative 
character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". 
tol 
the desired accuracy (convergence tolerance) for our algorithm. 
maxiter 
maximum number of iterations. 
tol.uniroot 
desired accuracy (convergence tolerance) for the

ES
: The effect size definition parameter for the k^{th} endpoint is defined as \frac{μ^{T}_{k}μ^{C}_{k}}{σ^{*}_{k}}, where σ^{*}_{k} refers to the standard deviation
of the population from which the different treatment groups were taken
and μ^{T}_{k}μ^{C}_{k} is the true mean difference between the test and the control group for the k^{th} group. We consider that: σ^{*}_{k}=\frac{σ^{2}_{k,T}+σ^{2}_{k,C}}{2}.
Adjusted TypeI error rate 
adjusted TypeI error rate. 
Sample size 
the required sample size. 
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
,
global.1m.analysis
,
indiv.1m.analysis
,
bonferroni.1m.ssc
1 2 3 4 5 6 7 8 9 10  # Sample size computation for the individual method
indiv.1m.ssc(method = "Known", ES = c(0.1, 0.2, 0.3), cor = diag(1, 3))
# Table 2 in our 2014 paper:
Sigma2 < matrix(c(5.58, 2, 1.24, 2, 4.29, 1.59, 1.24, 1.59, 4.09), ncol = 3)
sd2 < sqrt(diag(Sigma2))
cor2 < diag(1 / sd2) %*% Sigma2 %*% diag(1 / sd2)
mu2 < c(0.35, 0.28, 0.46)
m < 3
indiv.1m.ssc(method = "Known", ES = mu2 / sd2, cor = cor2)

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