Calculates the conditional error for all possible outcomes at the interim analysis (different number of responses)
using no "rest alpha" spending (difference between nominal alpha level and actual alpha level).
Usage
getD_none(design)
Arguments
design
a dataframe containing all critical values for a Simon's two-stage design defined by the colums "r1", "n1", "r", "n" and "p0".
r1 = critical value for the first stage (more than r1 responses needed to proceed to the second stage).
n1 = number of patients enrolled in the first stage.
r = critical value for the whole trial (more than r responses needed at the end of the study to reject the null hypothesis).
n = number of patients enrolled in the whole trial.
p0 = response probability under the null hypothesis.
References
Englert S., Kieser M. (2012): Adaptive designs for single-arm phase II trials in oncology. Pharmaceutical Statistics 11,241-249.
# NOT RUN {#Calculate a Simon's two-stage designdesign <- getSolutions()$Solutions[3,] #minimax-design for the default values.
ce_toOne <- getD_none(design)
ce_toOne
# }