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gsBoundCP() computes the total probability of crossing future upper
bounds given an interim test statistic at an interim bound. For each interim
boundary, assumes an interim test statistic at the boundary and computes the
probability of crossing any of the later upper boundaries.
See Conditional power section of manual for further clarification. See also Muller and Schaffer (2001) for background theory.
gsBoundCP(x, theta = "thetahat", r = 18)A list containing two vectors, CPlo and CPhi.
A vector of length x$k-1 with conditional powers of
crossing upper bounds given interim test statistics at each lower bound
A vector of length x$k-1 with conditional powers of
crossing upper bounds given interim test statistics at each upper bound.
An object of type gsDesign or gsProbability
if "thetahat" and class(x)!="gsDesign",
conditional power computations for each boundary value are computed using
estimated treatment effect assuming a test statistic at that boundary
(zi/sqrt(x$n.I[i]) at analysis i, interim test statistic
zi and interim sample size/statistical information of
x$n.I[i]). Otherwise, conditional power is computed assuming the
input scalar value theta.
Integer value (>= 1 and <= 80) controlling the number of numerical
integration grid points. Default is 18, as recommended by Jennison and
Turnbull (2000). Grid points are spread out in the tails for accurate
probability calculations. Larger values provide more grid points and greater
accuracy but slow down computation. Jennison and Turnbull (p. 350) note an
accuracy of r = 16. This parameter is normally
not changed by users.
Keaven Anderson keaven_anderson@merck.com
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,
gsCP
# set up a group sequential design
x <- gsDesign(k = 5)
x
# compute conditional power based on interim treatment effects
gsBoundCP(x)
# compute conditional power based on original x$delta
gsBoundCP(x, theta = x$delta)
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