A Practical Response Adaptive Block Randomization (RABR) Design with Analytic Type I Error Protection
To evaluate type I error rate, power, and operating characteristics of RABR via simulations.
You can install the released version of RABR from CRAN with:
install.packages("RABR")We provide an example of RABR with a continuous endpoint. One may refer to the vignette for more details.
library(RABR)
library(parallel)
library(doParallel)
#> Loading required package: foreach
#> Loading required package: iterators
RABR.fit = RABRcontinuous(
MeanVec = c(0.43, 0.48, 0.63, 1.2),
SdVec = c(1, 1, 1, 1),
M = 60,
N = 120,
R = c(8, 9, 2, 1),
Nitt = 1000,
Alpha = 0.025,
Ncluster = 2,
Seed = 12345,
MultiMethod = "dunnett")
##
## Probability of rejecting each elementary null
## hypothesis without multiplicity adjustment
print(RABR.fit$ProbUnadj)
#> [1] 0.027 0.093 0.877
##
## Probability of rejecting each elementary null
## hypothesis with multiplicity adjustment
print(RABR.fit$ProbAdj)
#> [1] 0.017 0.062 0.804
##
## Probability of selecting and confirming the
## efficacy of each active treatment group
print(RABR.fit$ProbAdjSelected)
#> [1] 0.001 0.007 0.802
##
## ProbAdjOverall Probability of rejecting at
## least one elementary null hypothesis
## with multiplicity adjustment
print(RABR.fit$ProbAdjOverall)
#> [1] 0.81
##
## ASN Average sample size of placebo and active
## treatment groups
print(RABR.fit$ASN)
#> [1] 39.107 40.746 21.432 18.715