GitHub - tian-yu-zhan/RABR: Simulations for Response Adaptive Block Randomization (RABR) Design (original) (raw)

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.

Installation

You can install the released version of RABR fromCRAN with:

Example

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