# The stopping boundaries based on the multiple outcomes criterion

### Description

The design function to sequentially monitor sample size and boundary based on Thall, Simon and Estey's criterion.

### Usage

1 | ```
MultPostP.design(type, nmax, a.vec, p0, theta, optimize)
``` |

### Arguments

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

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

`a.vec` |
the hyperparameter vector of the Dirichlet prior for the experimental drug. |

`p0` |
the prespecified reseponse rate for efficacy or toxicity. |

`theta` |
the cutoff probability: typically, |

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

### Value

`boundset` |
the boundaries set: |

### References

Thall, Peter F., Richard M. Simon, and Elihu H. Estey. (1995).
*Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes.*
*Statistics in medicine* **14.4**: 357-379.

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

### Examples

1 2 3 4 | ```
## Using vague prior Unif(0,1)
MultPostP.design(type="futility",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.05)
MultPostP.design(type="efficacy",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.9)
MultPostP.design(type="toxicity",nmax = 30,a.vec = c(1,1,1,1),p0 = 0.15, theta = 0.95)
``` |

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