power.mpe.atleast.one {MKpower} | R Documentation |
Power for at least One Endpoint with Known Covariance
Description
The function calculates either sample size or power for continuous multiple primary endpoints for at least one endpoint with known covariance.
Usage
power.mpe.atleast.one(K, n = NULL, delta = NULL, Sigma, SD, rho, sig.level = 0.05/K,
power = NULL, n.max = 1e5, tol = .Machine$double.eps^0.25)
Arguments
K |
number of endpoints |
n |
optional: sample size |
delta |
expected effect size |
Sigma |
A covariance of known matrix |
SD |
known standard deviations (length |
rho |
known correlations (length |
sig.level |
Significance level (Type I error probability) |
power |
optional: Power of test (1 minus Type II error probability) |
n.max |
upper end of the interval to be search for |
tol |
The desired accuracy |
Details
The function can be used to either compute sample size or power for continuous multiple primary endpoints with known covariance where a significant difference for at least one endpoint is expected. The implementation is based on the formulas given in the references below.
The null hypothesis reads \mu_{Tk}-\mu_{Ck}\le 0
for
all k\in\{1,\ldots,K\}
where Tk is treatment k,
Ck is control k and K is the number of co-primary endpoints.
One has to specify either n
or power
, the other parameter is
determined. Moreover, either covariance matrix Sigma
or standard
deviations SD
and correlations rho
must be given.
Value
Object of class power.mpe.test
, a list of arguments (including the
computed one) augmented with method and note elements.
Note
The function first appeared in package mpe, which is now archived on CRAN.
Author(s)
Srinath Kolampally, Matthias Kohl Matthias.Kohl@stamats.de
References
Sugimoto, T. and Sozu, T. and Hamasaki, T. (2012). A convenient formula for sample size calculations in clinical trials with multiple co-primary continuous endpoints. Pharmaceut. Statist., 11: 118-128. doi:10.1002/pst.505
Sozu, T. and Sugimoto, T. and Hamasaki, T. and Evans, S.R. (2015). Sample Size Determination in Clinical Trials with Multiple Endpoints. Springer Briefs in Statistics, ISBN 978-3-319-22005-5.
Examples
## compute power
power.mpe.atleast.one(K = 2, delta = c(0.2,0.2), Sigma = diag(c(1,1)), power = 0.8)
## compute sample size
power.mpe.atleast.one(K = 2, delta = c(0.2,0.2), Sigma = diag(c(2,2)), power = 0.9)
## known covariance matrix
Sigma <- matrix(c(1.440, 0.840, 1.296, 0.840,
0.840, 1.960, 0.168, 1.568,
1.296, 0.168, 1.440, 0.420,
0.840, 1.568, 0.420, 1.960), ncol = 4)
## compute power
power.mpe.atleast.one(K = 4, n = 60, delta = c(0.5, 0.75, 0.5, 0.75), Sigma = Sigma)
## equivalent: known SDs and correlation rho
power.mpe.atleast.one(K = 4, n = 60, delta = c(0.5, 0.75, 0.5, 0.75),
SD = c(1.2, 1.4, 1.2, 1.4),
rho = c(0.5, 0.9, 0.5, 0.1, 0.8, 0.25))