AdaptiveRandomisation {BayesianPlatformDesignTimeTrend} | R Documentation |

## AdaptiveRandomisation

### Description

This is a function doing the randomisation process. This Function generates the Sequence for patient allocation to each arm, patient outcomes.

### Usage

```
AdaptiveRandomisation(
Fixratio,
rand.algo,
K,
n.new,
randomprob,
treatmentindex,
groupwise.response.probs,
group,
armleft,
max.deviation,
trend_add_or_multip,
trend.function,
trend.effect,
ns,
Fixratiocontrol
)
```

### Arguments

`Fixratio` |
A indicator TRUE/FALSE |

`rand.algo` |
Randomisation algorithm: "Coin": Biased coin; "Urn": Urn method |

`K` |
Total number of arms at the beginning |

`n.new` |
The cohort size |

`randomprob` |
A named vector of randomisation probability to each arm |

`treatmentindex` |
The vector of treatment arm index excluding the control arm whose index is 0 |

`groupwise.response.probs` |
A matrix of response probability of each arm |

`group` |
The current stage |

`armleft` |
The number of treatment left in the platform (>2) |

`max.deviation` |
Tuning parameter of using urn randomisation method. |

`trend_add_or_multip` |
How time trend affects the true response probability: "add" or "mult" |

`trend.function` |
The function returns time trend effect regarding to different time trend pattern |

`trend.effect` |
The strength of time trend effect as a parameter in trend.function() |

`ns` |
A vector of accumulated number of patient at each stage |

`Fixratiocontrol` |
A numeric value indicating the weight of control in randomisation. Eg. 1 means equal randomisation, 2 means thw number of patients allocated to control is twice as large as other treatment arm. |

### Value

A list of patient allocation and patient outcome nstage: A vector of the number of patients allocated to each arm ystage: A vector of the patients outcome after treating with each arm znew: A vector of treatment index assigned to each patient in the current cohort ynew: A vector of outcome index record for each patient after treatment in the current cohort

### Author(s)

Ziyan Wang

### References

Mass weighted urn designâ€”a new randomization algorithm for unequal allocations. Zhao, Wenle. Contemporary clinical trials 43 (2015): 209-216.

### Examples

```
AdaptiveRandomisation(
Fixratio = FALSE,
rand.algo = "Urn",
K = 2,
n.new = 30,
randomprob = matrix(c(0.5, 0.5), ncol = 2, dimnames = list(c(),c("1","2"))),
treatmentindex = 1,
groupwise.response.probs = matrix(rep(c(0.4, 0.4), 5), byrow = TRUE, ncol = 2, nrow = 5),
group = 1,
armleft = 2,
max.deviation = 3,
trend_add_or_multip = "mult",
trend.function = function(ns, group, i, trend.effect) {delta = 0; return(delta)},
trend.effect = c(0, 0),
ns = c(30, 60, 90, 120, 150),
Fixratiocontrol = NA)
AdaptiveRandomisation(
Fixratio = TRUE,
rand.algo = "Urn",
K = 4,
n.new = 30,
randomprob = NA,
treatmentindex = c(1,3),
groupwise.response.probs = matrix(rep(c(0.4, 0.4,0.4, 0.4), 5), byrow = TRUE, ncol = 4, nrow = 5),
group = 1,
armleft = 3,
max.deviation = 3,
trend_add_or_multip = "mult",
trend.function = function(ns, group, i, trend.effect) {delta = 0; return(delta)},
trend.effect = c(0, 0),
ns = c(30, 60, 90, 120, 150),
Fixratiocontrol = 1)
```

*BayesianPlatformDesignTimeTrend*version 1.2.3 Index]