| 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)