BioInfo.Power {ADCT} | R Documentation |
Given the Biomarker-Informed design information, returns the overall power and probability of the arm is selected as the winner.
BioInfo.Power(uCtl, u0y, u0x, rhou, suy, sux, rho, sy, sx, Zalpha, N1, N, nArms, nSims)
uCtl |
mean value for the control group. |
u0y |
mean parameter of the group 1 for the parent model. |
u0x |
mean parameter of the group 2 for the parent model. |
rhou |
correlation coefficient between two groups for the parent model. |
suy |
standard deviation of the group 1 for the parent model. |
sux |
standard deviation of the group 2 for the parent model. |
rho |
correlation coefficient between two groups for the lower level model. |
sy |
standard deviation of the group 1 for the lower level model. |
sx |
standard deviation of the group 2 for the lower level model. |
Zalpha |
crtical point for rejection. |
N1 |
sample size per group at interim analysis. |
N |
sample size per group at final analysis. |
nArms |
number of active groups. |
nSims |
number of simulation times. |
The evaluated power and probability of selecting the arm as the winner.
Yalin Zhu
Chang, M. (2014). Adaptive design theory and implementation using SAS and R. CRC Press.
## Determine critical value Zalpha for alpha (power) =0.025
u0y=c(0,0,0); u0x=c(0,0,0)
BioInfo.Power(uCtl=0, u0y, u0x, rhou=1, suy=0, sux=0, rho=1, sy=4, sx=4,
Zalpha=2.772, N1=100, N=300, nArms=3, nSims=1000)
## Power simulation
u0y=c(1,0.5,0.2)
u0x=c(2,1,0.5)
BioInfo.Power(uCtl=0, u0y, u0x, rhou=0.2, suy=0.2, sux=0.2, rho=0.2, sy=4, sx=4,
Zalpha=2.772, N1=100, N=300, nArms=3, nSims=500)