altprimary | R Documentation |
Calculates power and sample size for the case of comparing two groups on the means of K continuous endpoints and concluding that the trial is a 'success' if the null hypothesis is rejected for at least one of the K endpoints. All mean differences must be specified as positive; the scale for some outcomes may need to be reversed to meet this condition. All tests are assumed to be upper-tailed, one-sided tests. Can solve for power, n1, n.ratio or alpha.
To use a Bonferroni correction for multiple comparisons, specify alpha as the desired familywise error rate (FWER) divided by K. For example, for one-sided FWER of 0.025 and K = 2 endpoints, specify alpha as 0.0125.
Either sd and rho or Sigma must be specified.
A known covariance matrix is assumed, which can result in a slight overestimate of power and underestimate of required sample size.
altprimary(
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 (type 1 error rate) for each test; defaults to 0.025. A one-sided test is assumed. |
power |
The specified level of power. |
v |
Either TRUE for verbose output or FALSE (default) to output computed argument only. |
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).
altprimary(K = 2, n1 = 100, delta = c(0.4, 0.5), sd = c(1, 1), rho = 0.3,
alpha = 0.025 / 2, power = NULL)
Sigma <- matrix(c(1, 0.3, 0.3, 0.3, 1, 0.3, 0.3, 0.3, 1), nrow = 3, ncol = 3)
altprimary(K = 3, n1 = 100, delta = c(0.2, 0.2, 0.4), Sigma = Sigma,
alpha = 0.025 / 3, power = NULL)
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