gsCP {gsDesign} | R Documentation |
gsCP()
takes a given group sequential design, assumes an interim z-statistic
at a specified interim analysis and computes boundary crossing probabilities at future planned analyses.
gsCP(x, theta=NULL, i=1, zi=0, r=18)
x |
An object of type gsDesign or gsProbability |
theta |
theta value(s) at which conditional power is to be computed; if NULL ,
an estimated value of theta based on the interim test statistic (zi/sqrt(x$n.I[i]) ) as well as at x$theta
is computed. |
i |
analysis at which interim z-value is given |
zi |
interim z-value at analysis i (scalar) |
r |
Integer value controlling grid for numerical integration as in Jennison and Turnbull (2000);
default is 18, range is 1 to 80.
Larger values provide larger number of grid points and greater accuracy.
Normally r will not be changed by the user. |
See Conditional power section of manual for further clarification. See also Muller and Schaffer (2001) for background theory.
An object of the class gsProbability
.
Based on the input design and the interim test statistic, the output object has bounds for test statistics
computed based on observations after interim i
that are equivalent to the original design crossing boundaries conditional
on the interim test statistic value input.
Boundary crossing probabilities are computed for the input
theta values.
The manual is not linked to this help file, but is available in library/gsdesign/doc/gsDesignManual.pdf in the directory where R is installed.
Keaven Anderson keaven_anderson@merck.
Jennison C and Turnbull BW (2000), Group Sequential Methods with Applications to Clinical Trials. Boca Raton: Chapman and Hall.
Muller, Hans-Helge and Schaffer, Helmut (2001), Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and classical group sequential approaches. Biometrics;57:886-891.
gsDesign
, gsProbability
, gsBoundCP
# set up a group sequential design x <- gsDesign(k=5) x # assuming a z-value of .5 at analysis 2, what are conditional # boundary crossing probabilities for future analyses # assuming theta values from x as well as a value based on the interim # observed z CP <- gsCP(x, i=2, zi=.5) CP # summing values for crossing future upper bounds gives overall # conditional power for each theta value CP$theta CP$upper$prob