TwoSampleBernoulli.Design {BayesDIP} | R Documentation |

## Two sample Bernoulli model - Trial Design

### Description

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.

### Usage

```
TwoSampleBernoulli.Design(
prior,
nmin = 10,
nmax = 200,
p1,
p2,
d = 0,
ps = 0.95,
pf = 0.05,
power = 0.8,
t1error = 0.05,
alternative = c("less", "greater"),
seed = 202209,
sim = 500
)
```

### Arguments

`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 total sample size for two treatment groups. |

`nmax` |
The stop searching total sample size for two treatment groups. |

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

`p2` |
The response rate of the compared 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. |

### Value

A list of the arguments with method and computed elements

### Examples

```
# with traditional Bayesian prior Beta(1,1)
TwoSampleBernoulli.Design(list(2,1,1), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0,
ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater",
seed = 202210, sim = 10)
# with DIP
TwoSampleBernoulli.Design(list(1,0,0), nmin = 100, nmax = 120, p1 = 0.5, p2 = 0.3, d = 0,
ps = 0.90, pf = 0.05, power = 0.8, t1error = 0.05, alternative = "greater",
seed = 202210, sim = 10)
```

*BayesDIP*version 0.1.1 Index]