gsCP {gsDesign}R Documentation

2.4: Conditional Power Computation

Description

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.

Usage

gsCP(x, theta=NULL, i=1, zi=0, r=18)

Arguments

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.

Details

See Conditional power section of manual for further clarification. See also Muller and Schaffer (2001) for background theory.

Value

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.

Note

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.

Author(s)

Keaven Anderson keaven_anderson@merck.

References

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.

See Also

gsDesign, gsProbability, gsBoundCP

Examples

# 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 

[Package gsDesign version 2.0-5 Index]