RABRcontinuous {RABR} | R Documentation |
Simulate RABR for continuous endpoints to evaluate operating characteristics
Description
Simulate RABR for continuous endpoints to evaluate operating characteristics
Usage
RABRcontinuous(
MeanVec,
SdVec,
M,
N,
R,
Nitt,
Alpha,
Ncluster = 1,
Seed = 12345,
MultiMethod
)
Arguments
MeanVec |
Vector of response mean for placebo and active treatment groups. |
SdVec |
Vector of standard deviation for placebo and active treatment groups. |
M |
Total sample size of burn-in period. |
N |
Total sample size of RABR. Must be larger than M. |
R |
Randomization vector for placebo and active treatment groups. |
Nitt |
Number of simulation iterations. |
Alpha |
One-sided significance level. |
Ncluster |
Number of clusters for parallel computing. |
Seed |
Random seed. |
MultiMethod |
Multiplicity adjustment method. Must be one of the following values "holm", "hochberg", "hommel", "bonferroni", or "dunnett". |
Details
The MeanVec
is a vector of response mean for placebo and active treatment groups, while SdVec
is for standard deviation. They should be with the same length. The current package supports 2 or 3 active treatment groups. Note that a larger response corresponds to a better outcome.
The M
is the total sample size of burn-in period with equal randomization. The total sample size N
should be larger than N. The choice of M
can be selected by comparing simulations from several candidate values. The R
is a pre-specified randomization vector, where the first element is for placebo, and the next one for the best performing group, up to the worst performing group.
The Alpha
is the one-sided significance level. The MultiMethod
can be set at "holm" for Holm, "hochberg" for Hochberg, "hommel" for Hommel, "bonferroni" for Bonferroni, or "dunnett" for Dunnett procedures.
Value
ProbUnadj: Probability of rejecting each elementary null hypothesis without multiplicity adjustment
ProbAdj: Probability of rejecting each elementary null hypothesis with multiplicity adjustment
ProbAdjSelected: Probability of selecting and confirming the efficacy of each active treatment group
ProbAdjOverall: Probability of rejecting at least one elementary null hypothesis with multiplicity adjustment
ASN: Average sample size of placebo and active treatment groups
Author(s)
Tianyu Zhan (tianyu.zhan.stats@gmail.com)
References
Zhan, T., Cui, L., Geng, Z., Zhang, L., Gu, Y., & Chan, I. S. (2021). A practical response adaptive block randomization (RABR) design with analytic type I error protection. Statistics in Medicine, 40(23), 4947-4960.
Cui, L., Zhan, T., Zhang, L., Geng, Z., Gu, Y., & Chan, I. S. (2021). An automation-based adaptive seamless design for dose selection and confirmation with improved power and efficiency. Statistical Methods in Medical Research, 30(4), 1013-1025.
Examples
## Consider an example with three active treatment
## groups and a placebo. Suppose that the response
## mean for placebo is 0.43 and 0.48, 0.63, and 1.2
## for three active treatment groups. The standard
## deviation is 1 for all groups. The total sample
## size is N = 120 with a burn-in period M = 60. We
## use the randomization vector of (8, 9, 2, 1),
## which means that placebo, the best performing
## group, the second-best group, and the worst group
## have randomization probabilities 8/20, 9/20, 2/20
## 1/20, respectively. The one-sided significance
## level is considered at 2.5%. Nitt = 100 is for
## demonstration, and should be increased to 10^5
## in practice.
##
library(parallel)
library(doParallel)
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 = 100,
Alpha = 0.025,
Ncluster = 2,
Seed = 12345,
MultiMethod = "dunnett")
##
## Probability of rejecting each elementary null
## hypothesis without multiplicity adjustment
print(RABR.fit$ProbUnadj)
##
## Probability of rejecting each elementary null
## hypothesis with multiplicity adjustment
print(RABR.fit$ProbAdj)
##
## Probability of selecting and confirming the
## efficacy of each active treatment group
print(RABR.fit$ProbAdjSelected)
##
## ProbAdjOverall Probability of rejecting at
## least one elementary null hypothesis
## with multiplicity adjustment
print(RABR.fit$ProbAdjOverall)
##
## ASN Average sample size of placebo and active
## treatment groups
print(RABR.fit$ASN)