OneSampleNormal2.Design {BayesDIP} | R Documentation |

## One sample Normal model with two-parameter unknown - both mean and variance unknown

### 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

```
OneSampleNormal2.Design(
prior,
nmin = 10,
nmax = 100,
mu0,
mu1,
var0,
var,
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 ; Normal(mu0,var/k) and var ~ Inverse-Gamma(v/2, v*var0/2) where mu0 = prior mean, k = sample size of prior observations (Normal prior), v = sample size of prior observations (Gamma prior), var0 = prior sample variance
The second and third elements of the list are the parameters k and v, respectively. |

`nmin` |
The start searching sample size |

`nmax` |
The stop searching sample size |

`mu0` |
The null mean value, which could be taken as the standard or current mean. |

`mu1` |
The mean value of the new treatment. |

`var0` |
The prior sample variance |

`var` |
The variance |

`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)
OneSampleNormal2.Design(list(2,2,1), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95,
var0=225, var=225, d = 0, ps = 0.95, pf = 0.05,
power = 0.8, t1error = 0.05, alternative = "less",
seed = 202210, sim = 10)
# with DIP
OneSampleNormal2.Design(list(1,0,0), nmin = 10, nmax = 100, mu0 = 100, mu1 = 95,
var0=225, var=225, d = 0, ps = 0.95, pf = 0.05,
power = 0.8, t1error = 0.05, alternative = "less",
seed = 202210, sim = 10)
```

*BayesDIP*version 0.1.1 Index]