OneArmPhaseTwoStudy (version 1.0.3)

getN2: Calculates the number of patients which should be enrolled in the second stage.

Description

Calculates the number of patients which should be enrolled in the second stage if the conditional power should be altert to "cp".

Usage

getN2(cp, p1, design, k, mode = 0, alpha = 0.05)

Arguments

cp

conditional power to which the number of patients for the second stage should be adjusted.

p1

response probability under the alternative hypothesis.

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.

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).

• 0 = "rest alpha" is not used.

• 1 = "rest alpha" is spent proportionally.

• 2 = "rest alpha" is spent equally.

• 3 = "rest alpha" is spent only to the worst case scenario (minimal number of responses at the interim analysis so that the study can proceed to the second stage).

alpha

overall significance level the trial was planned for.

References

Englert S., Kieser M. (2012): Adaptive designs for single-arm phase II trials in oncology. Pharmaceutical Statistics 11,241-249.

Examples

# NOT RUN {
#Calculate a Simon's two-stage design
design <- getSolutions()$Solutions[3,] #minimax-design for the default values.

#Assume we only observed 3 responses 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.

#set k to 3 (only 3 responses observed so far)
k = 3

# Assume we spent the "rest alpha" proportionally in the planning phase
# there for we set "mode = 1".
n2 <- getN2(cp = 0.8, design$p1, design, k, mode = 1, alpha = 0.05)
n2
# }