OneSamplePoisson.Design {BayesDIP} | R Documentation |

## One sample Poisson 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

```
OneSamplePoisson.Design(
prior,
nmin = 10,
nmax = 100,
m0,
m1,
d = 0,
ps,
pf,
power = 0.8,
t1error = 0.05,
alternative = c("less", "greater"),
seed = 202209,
sim = 1000
)
```

### 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 ; Gamma(a,b), where a = shape, b = rate
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 |

`m0` |
The null event rate, which could be taken as the standard or current event rate. |

`m1` |
The event 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 expected 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 Gamma(0.5,0.001)
OneSamplePoisson.Design(list(2,0.5,0.001), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0,
ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less",
seed = 202210, sim = 10)
# with DIP
OneSamplePoisson.Design(list(1,0,0), nmin = 10, nmax=100, m0 = 5, m1 = 4, d = 0,
ps = 0.95, pf = 0.05, power = 0.80, t1error=0.05, alternative = "less",
seed = 202210, sim = 10)
```

*BayesDIP*version 0.1.1 Index]