| coprimary.z | R Documentation | 
Computes power for test involving multiple co-primary continuous endpoints, assuming that the covariance matrix (variances and covariances between endpoints) is known and therefore z-based test statistics will be used. Studies with co-primary endpoints use “all-or-none” testing procedures and only declare the trial to be a “success” if all endpoints are affirmed. All true mean differences must be positive (the scale for some outcomes may need to be reversed to meet this condition) and upper-tailed one-sided tests are assumed. For the more realistic case that the covariance matrix is not known, see coprimary.t.
Either sd and rho or Sigma must be specified.
coprimary.z(
  K,
  n1 = NULL,
  n.ratio = 1,
  delta = NULL,
  Sigma,
  sd,
  rho,
  alpha = 0.025,
  power = NULL,
  v = FALSE
)
K | 
 The number of endpoints.  | 
n1 | 
 The sample size for group 1.  | 
n.ratio | 
 The ratio n2/n1 between the sample sizes of two groups; defaults to 1 (equal group sizes).  | 
delta | 
 A vector of length K of the true mean differences mu1k - mu2k; must all be positive.  | 
Sigma | 
 The covariance matrix of the K outcomes, of dimension K x K.  | 
sd | 
 A vector of length K of the standard deviations of the K outcomes.  | 
rho | 
 A vector of length 0.5K(K-1) of the correlations among the K outcomes.  | 
alpha | 
 The significance level or type 1 error rate; defaults to 0.025. A one-sided test is assumed.  | 
power | 
 The specified level of power.  | 
v | 
 Either TRUE for verbose output or FALSE to output computed argument only.  | 
See Crespi et al. (2025) for more details. This function is based on the power.known.var function from the mpe R package and material from Sozu T, Sugimoto T, Hamasaki T, Evans SR. (2015) Sample Size Determination in Clinical Trials with Multiple Endpoints. Springer International Publishing, Switzerland.
A list of the arguments (including the computed one).
coprimary.z(K = 2, n1 = 100, delta = c(0.4, 0.5), sd = c(1, 1), rho = 0.3,
alpha = 0.025, power = NULL)
Sigma <- matrix(c(1, 0.3, 0.3, 0.3, 1, 0.3, 0.3, 0.3, 1), nrow = 3, ncol = 3)
coprimary.z(K = 3, n1 = NULL, delta = c(0.2, 0.3, 0.4), Sigma = Sigma, alpha = 0.025, power = 0.8)
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