# The stopping boundaries based on the predictive probability criterion

### Description

The design function to sequentially monitor sample size and boundary based on Lee and Liu's criterion.

### Usage

1 | ```
PredP.design(type, nmax, a, b, p0, theta_t, theta, optimize)
``` |

### Arguments

`type` |
type of boundaries: "efficacy" or "futility". |

`nmax` |
the maximum number of patients treated by the experimental drug. |

`a` |
the hyperparameter (shape1) of the Beta prior for the experimental drug. |

`b` |
the hyperparameter (shape2) of the Beta prior for the experimental drug. |

`p0` |
the the response rate for the standard drug. |

`theta_t` |
the cutoff probability for efficacy including future patients; typically, |

`theta` |
the cutoff probability: typically, |

`optimize` |
logical value, if optimize=TRUE, then only output the minimal sample size for the same number of futility and efficacy boundaries. |

### Value

`boundset` |
the boundaries set: |

### References

Lee, J. J., Liu, D. D. (2008).
A predictive probability design for phase II cancer clinical trials.
*Clinical Trials* **5**: 93-106.

Yin, G. (2012).
*Clinical Trial Design: Bayesian and Frequentist Adaptive Methods.*
New York: Wiley.

### Examples

1 2 | ```
PredP.design(type = "futility", nmax=40, a=1, b=1, p0=0.3, theta=0.05)
PredP.design(type = "efficacy", nmax=40, a=1, b=1, p0=0.3, theta=0.9)
``` |