Description Usage Arguments Value Author(s) References Examples

Given the Biomarker-Informed design information, returns the overall power and probability of the arm is selected as the winner.

1 | ```
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*.

1 2 3 4 5 6 7 8 9 | ```
## 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)
``` |

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