BAC_binom {BACCT} | R Documentation |

Calling JAGS to implement BAC for binary responses

```
BAC_binom(yh, nh, n1, n2, y1.range = 0:n1, y2.range = 0:n2, n.chain = 5,
tau.alpha = 0.001, tau.beta = 0.001, prior.type = "nonmixture",
criterion.type = c("diff", "prob"), prob.threshold, sim.mode = c("full",
"express"))
```

`yh` , `nh` |
Vector of the numbers of events (subjects) in the historical trial(s). Must be of equal length. |

`n1` , `n2` |
Number of subjects in the control or treatment arm of the current trial. |

`y1.range` , `y2.range` |
Number of events in control or treatment arm of the current trial. See "Details". |

`n.chain` |
Controls the number of posterior samples. Each chain contains 20,000 samples. |

`tau.alpha` , `tau.beta` |
Hyperparameters of the inverse gamma distribution controling the extent of borrowing. |

`prior.type` |
Type of prior on control groups. Currenly, only the inverse-gamma prior is implemented. |

`criterion.type` |
Type of posterior quantities to be monitored. See "Details." |

`prob.threshold` |
For |

`sim.mode` |
Simulation duration reduces greatly in |

There are two types of posterior quantities for
`criterion.type`

argument. With `"diff"`

option, the quantity
computed is `p_{T} - p_{C}`

; with `"prob,"`

such quantity is
`pr(p_{T} - p_{C}>\Delta)`

, where `\Delta`

is specified by
`prob.threshold`

argument.

By default, `y1.range`

and `y2.range`

cover all possible outcomes
and should be left unspecified in most cases. However, when `n1`

and/or `n2`

is fairly large, it is acceptable to use a reduced range
that covers the outcomes that are most likely (e.g., within 95% CI) to be
observed. This may help shorten the time to run MCMC.

Another way that can greatly shorten the MCMC running time is to specify
`"express"`

mode in `sim.mode`

argument. Express mode reduces the
number of simulations from `length(y1.range)*length(y2.range)`

to
`length(y1.range)+length(y2.range)`

. Express mode is proper when the
treatment arm rate is independent of control arm rate.

An object of class "BAC".

Hongtao Zhang

```
## Not run:
library(BACCT)
#borrow from 3 historical trials#
yh = c(11,300,52);nh = c(45,877,128)
#specify current trial sample sizes#
n1 = 20;n2 = 30
#Difference criterion type in full simulation mode#
obj1 = BAC_binom(yh=yh,nh=nh,n1=n1,n2=n2,n.chain=5,
criterion.type="diff",sim.mode="full")
#Probability criterion type in express simulation mode#
obj2 = BAC_binom(yh=yh,nh=nh,n1=n1,n2=n2,n.chain=5,
criterion.type="prob",prob.threshold=0.1,sim.mode="express")
#S3 method for class "BAC"
summary(obj1)
## End(Not run)
```

[Package *BACCT* version 1.0 Index]