Description Usage Arguments Value Author(s) References See Also Examples
This function computes the sample size with a global method in the context of m multiple continuous endpoints. Two groups are considered: C for control and T for treatment. The clinical aim is to be able to detect a mean difference between the test and the control product for at least one endpoint among m. This method is based on a multivariate model with co-variates taking into account the correlations between the endpoints.
1 2 |
method |
either "Model" if no co-variates are involved and "Adj.Model" for a model with co-variates. |
mean.diff |
vector of the mean differences of the |
sd |
vector of the standard deviations of the |
cor |
correlation matrix between the endpoints. These are assumed identical for both groups. |
v |
v is a p\times1 vector whose l^{th} component is v_{l}=\bar{a}_{l}^C-\bar{a}_l^T, where p is the number of adjustment variables, and \bar{a}_{l}^{i} is the mean of the adjustment variable a_{l} for the group i, i = C, T. |
M |
M is a p\times p matrix with general term M_{l,l'}=≤ft(\overline{a_la_{l'}}^C-\bar{a}_l^C\bar{a}_{l'}^C\right)+≤ft(\overline{a_{l}a_{l'}}^T-\bar{a}_l^T\bar{a}_{l'}^T\right). |
power |
value which corresponds to the chosen power. |
alpha |
value which correponds to the chosen Type-I error rate bound. |
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.analysis
,
indiv.1m.ssc
,
indiv.1m.analysis
,
bonferroni.1m.ssc
1 2 3 4 5 6 7 8 9 10 11 | # Sample size computation for the global method
global.1m.ssc(method = "Adj.Model", mean.diff = c(0.1, 0.2, 0.3), sd =
c(1, 1, 1), cor = diag(1, 3), v = -0.2, M = 0.46)
# 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
global.1m.ssc(method = "Model", mean.diff = mu2, sd = sd2, cor = cor2)
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