brada {brada} | R Documentation |

## brada

### Description

Performs a Bayesian response-adaptive design analysis for trials with a binary endpoint.

### Usage

```
brada(a0=1,b0=1,Nmax=40,batchsize=5,nInit,p_true,p0,p1,
theta_T=0.90,theta_L=0.1,theta_U=1,nsim=100,
seed=42,method="PP",refFunc="flat",nu=0,
shape1=1,shape2=1,truncation=1,cores=2)
```

### Arguments

`a0` |
shape1 parameter of the beta prior. |

`b0` |
shape2 parameter of the beta prior. |

`Nmax` |
Maximum trial size. |

`batchsize` |
sample size after which an interim analysis is performed. |

`nInit` |
Initial sample size at which the first interim analysis is performed. |

`p_true` |
True binary response probability used for simulation. |

`p0` |
Right boundary of the null hypothesis to be tested. |

`p1` |
Left boundary of the alternative hypothesis to be tested. |

`theta_T` |
Threshold used in the designs for including trajectories as evidential. |

`theta_L` |
Stopping threshold for futility. |

`theta_U` |
Stopping threshold for efficacy. |

`nsim` |
Number of Monte Carlo iterations. |

`seed` |
Random number generator seed. |

`cores` |
Number of CPU cores to be used for computation. Defaults to 2, but 4 or larger is recommended. |

`method` |
Can be either "PP" or "PPe", depending on whether the predictive probability approach or the predictive evidence value design is desired. Note that the former is a special case of the latter. |

`refFunc` |
A string, either "flat", "beta", "binaryStep", "relu", "palu" or "lolu". See vignettes for explanation. |

`nu` |
A numeric value larger or equal to zero, indicating which evidence threshold if used in the predictive evidence value design. |

`shape1` |
shape1 parameter of the beta reference function, if used. |

`shape2` |
shape2 parameter of the beta reference function, if used. |

`truncation` |
Truncation point in case an artificial neural network reference function is used. |

### Value

Returns an object of class brada.

### Author(s)

Riko Kelter

### Examples

```
pp_design = brada(Nmax = 30, batchsize = 5, nInit = 10,
p_true = 0.2 , p0 = 0.2, p1 = 0.2,
nsim = 10,
a0 = 1, b0 = 1,
theta_T = 0.90, theta_L = 0.1, theta_U = 1,
method = "PP",
cores = 2)
summary(pp_design)
```

*brada*version 1.0 Index]