Description Usage Arguments References See Also Examples
Calculates the conditional power for a given Simon's twostage design in the interim analysis if the number of patients which should be enrolled in the second stage is altert to "n2".
1 
n2 
number of patients to be enrolled in the second stage of the study. 
p1 
response probability under the alternative hypothesis 
design 
a dataframe containing all critical values for a Simon's twostage design defined by the colums "r1", "n1", "r", "n" and "p0".

k 
number of responses observed at the interim analysis. 
mode 
a value out of {0,1,2,3} dedicating the methode spending the "rest alpha" (difference between nominal alpha level and actual alpha level for the given design).

alpha 
overall significance level the trial was planned for. 
Englert S., Kieser M. (2012): Adaptive designs for singlearm phase II trials in oncology. Pharmaceutical Statistics 11,241249.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  #Calculate a Simon's twostage design
design < getSolutions()$Solutions[3,] #minimaxdesign for the default values.
#Assume 3 responses were observed in the interim analysis.
#Therefore the conditional power is only about 0.55.
#In order to raise the conditional power to 0.8 "n2" has to be increased.
#get the current "n2"
n2 < design$n  design$n1
#set k to 3 (only 3 responses observed so far)
k = 3
#get the current conditional power
cp < getCP(n2, design$p1, design, k, mode = 1, alpha = 0.05)
cp
#increase n2 until the conditional power is larger than 0.8
while(cp < 0.8){
n2 < n2 + 1
# Assume we spent the "rest alpha" proportionally (in the planning phase)
# therefore we set "mode = 1".
cp < getCP(n2, design$p1, design, k, mode = 1, alpha = 0.05)
}
n2

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