OneSampleBernoulli.Design {BayesDIP} | R Documentation |

Calculate the minimum planned sample size under an admissible design. The users decide the power and type-I-error, and pick the efficacy and futility boundaries. If there are no admissible design based on controlled type-I-error, then default to output the designs with the lowest type-I-error and at least the user-defined (e.g. 80%) power.

```
OneSampleBernoulli.Design(
prior,
nmin = 10,
nmax = 100,
p0,
p1,
d = 0,
ps,
pf,
power = 0.8,
t1error = 0.05,
alternative = c("less", "greater"),
seed = 202209,
sim = 1000
)
```

`prior` |
A list of length 3 containing the distributional information of the prior. The first element is a number specifying the type of prior. Options are DIP ; Beta(a,b), where a = shape, b = scale
The second and third elements of the list are the parameters a and b, respectively. |

`nmin` |
The start searching sample size |

`nmax` |
The stop searching sample size |

`p0` |
The null response rate, which could be taken as the standard or historical rate. |

`p1` |
The response rate of the new treatment. |

`d` |
The target improvement (minimal clinically meaningful difference). |

`ps` |
The efficacy boundary (upper boundary). |

`pf` |
The futility boundary (lower boundary). |

`power` |
The power to achieve. |

`t1error` |
The controlled type-I-error. |

`alternative` |
less (lower values imply greater efficacy) or greater (larger values imply greater efficacy). |

`seed` |
The seed for simulations. |

`sim` |
The number of simulations. |

A list of the arguments with method and computed elements.

```
# with traditional Bayesian prior Beta(1,1)
OneSampleBernoulli.Design(list(2,1,1), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
seed = 202210, sim = 10)
# with DIP
OneSampleBernoulli.Design(list(1,0,0), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
seed = 202210, sim = 10)
```

[Package *BayesDIP* version 0.1.1 Index]