next_allocation_rate_BAR {BAR} | R Documentation |

Calculate updated allocation probability for each arm based on the accumulative data with binary outcomes

```
next_allocation_rate_BAR(n, success_count, tot_num,
power_c = "n/2N",
lower_bound = .05,
control_arm = "",
seed = 100)
```

`n` |
the number of patients enrolled for each arm |

`success_count` |
the number of responders for each arm |

`tot_num` |
the total number of patients enrolled for the trial. If this number cannot be pre-planned, the user can choose argument "power_c" to be numeric instead of "n/2N". In this case, even if the "tot_num" is given a number, this number will not be used |

`power_c` |
the power correction of allocation probability. The default value is power_c = "n/2N" and can also be numeric, e.g., power_c = .5 |

`lower_bound` |
the lower bound of the allocation probability. It must between 0 and |

`control_arm` |
if this argument is "fixed", then allocation probability of control arm (the first slot)
will be fixed to |

`seed` |
the seed. The default value is seed = 100 |

We show how the updated allocation probabilities for each arm are calculated.

Treatments are denoted by `k = 1,\ldots,K`

.`\;N`

is the total sample size. If no burn-in(s), the BAR will be initiated start of a study, that is, for each enrolled patient, `n = 1,\ldots,N,`

the BAR will be used to assign each patient. Denoting the true unknown response rates of `K`

treatments by `\pi_{1},\ldots,\pi_{K},\;`

we can compute `K`

posterior probabilities: `r_{k,n} = Pr(\pi_{k} = max\{\pi_{1},\ldots,\pi_{K}\}\;|\;Data_{n})`

, here, `n`

refers to the `n`

-th patient and `k`

refers to the `k`

-th arm. We calculate the updated probabilities of the BAR algorithm according to the following steps.

`\;`

Step 1: (Normalization) Normalize `r_{k,n}`

as `r_{k,n}^{(c)} = \frac{(r_{k,n})^{c}}{\sum_{j=1}^{K}(r_{j,n})^{c}}`

, here `\;c = \frac{n}{2N}`

.

Step 2: (Restriction) To avoid the BAR sticking to very low/high probabilities, a restriction rule to the posterior probability `r_{k,n}^{(c)}`

will be applied:

`Lower\;Bound \le r_{k,n}^{(c)} \le 1 - (K - 1) \times Lower\;Bound,`

`0 \le Lower\;Bound \le \frac{1}{K}`

After restriction, the posterior probability is denoted as `r_{k,n}^{(c,re)}`

.

Step 3: (Re-normalization) Then, we can have the updated allocation probabilities by the BAR denoted as:

`r_{k,n}^{(f)} = \frac{r_{k,n}^{(c,re)}\times(\frac{r_{k,n}^{(c,re)}}{\frac{n_{k}}{n}})^{2}}{\sum_{j=1}^{K}\{r_{j,n}^{(c,re)}\times(\frac{r_{j,n}^{(c,re)}}{\frac{n_{j}}{n}})^{2}\}}`

where `n_{k}`

is the number of patients enrolled on arm `k`

up-to-now.

Step 4: (Re-restriction) Finally, restricts again by using

`Lower\;Bound \le r_{k,n}^{(f)} \le 1 - (K-1) \times Lower\;Bound,`

`0 \le Lower\;Bound \le \frac{1}{K}`

and denote `r_{k,n}^{(ff)}`

as the allocation probability used in the BAR package.

`next_allocation_rate_BAR()`

returns the updated allocation probability for each arm

Chia-Wei Hsu, Haitao Pan

Wathen JK, Thall PF. A simulation study of outcome adaptive randomization in multi-arm clinical trials. Clin Trials. 2017 Oct; 14(5): 432-440. doi: 10.1177/1740774517692302.

Xiao, Y., Liu, Z. & Hu, F. Bayesian doubly adaptive randomization in clinical trials. Sci. China Math. 60, 2503-2514 (2017). doi: 10.1007/s11425-016-0056-1.

Hu F, Zhang L X. Asymptotic properties of doubly adaptive biased coin designs for multi-treatment clinical trials. Ann Statist, 2004, 30: 268–301.

```
## power_c = "n/2N"
next_allocation_rate_BAR(n = c(30, 30, 30),
success_count = c(5, 6, 12),
tot_num = 150)
## power_c = .5
next_allocation_rate_BAR(n = c(30, 30, 30),
success_count = c(5, 6, 12),
tot_num = 150, power_c = .5)
```

[Package *BAR* version 0.1.1 Index]