carat (version 1.1)

StrPBR: Stratified Permuted Block Randomization

Description

Allocates patients to one of two treatments using stratified permuted block randomization proposed by Zelen M (1974) <Doi: 10.1016/0021-9681(74)90015-0>.

Usage

# S3 method for carandom
StrPBR(data, bsize = 4)

Arguments

data

a dataframe. A row of the dataframe contains the covariate profile of a patient.

bsize

the block size for stratified randomization. It is required to be a multiple of 2. The default is 4.

Value

It returns an object of class "carandom".

The functions print is used to obtain results. The generic accessor functions Cov_Assig, Diff, data, All strata and others extract various useful features of the value returned by StrPBR.

An object of class "carandom" is a list containing at least the following components:

cov_num

the number of covariates.

n

the number of patients.

Cov_Assign

a (cov_num + 1) * n matrix containing covariate profiles for all patients and corresponding assignments. The \(i\)th column represents the \(i\)th patient. The first cov_num rows include patients' covariate profiles, and the last row contains the assignments.

All strata

a matrix containing all strata involved.

Diff

a matrix with only one column. There are final differences at the overall, within-stratum, and marginal levels.

Data Type

the data type. Real or Simulated.

Details

Different covariate profiles are defined to be strata, and then permuted block randomization is applied to each stratum. It works efficiently when the number of strata is small, but when the number of strata increases, the stratified permuted block randomization fails to obtain balance between two treatments.

Permuted-block randomization, or blocking, is used to balance treatment arms within a block so that there are the same number of subjects in each treatment arm. A block contains the same number of each treatment and blocks of different sizes are combined to make up the randomization list.

References

Zelen M. The randomization and stratification of patients to clinical trials[J]. Journal of chronic diseases, 1974, 27(7): 365-375.

See Also

See StrPBR.sim for allocating patients with covariate data generating mechanism. See StrPBR.ui for the command-line user interface.

Examples

# NOT RUN {
# a simple use
## Real Data
## creat a dataframe
df <- data.frame("gender" = sample(c("female", "male"), 100, TRUE, c(1 / 3, 2 / 3)), 
                 "age" = sample(c("0-30", "30-50", ">50"), 100, TRUE), 
                 "jobs" = sample(c("stu.", "teac.", "others"), 100, TRUE), 
                 stringsAsFactors = TRUE)
Res <- StrPBR(data = df, bsize = 4)
## view the output
Res
# }
# NOT RUN {
## view all patients' profile and assignments
Res$Cov_Assig
# }
# NOT RUN {
## Simulated data
cov_num <- 3
level_num <- c(2, 3, 3)
pr <- c(0.4, 0.6, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3)
Res.sim <- StrPBR.sim(n = 100, cov_num, level_num, pr)
## view the output
Res.sim
# }
# NOT RUN {
## view the detials of difference
Res.sim$Diff
# }
# NOT RUN {
# }
# NOT RUN {
N <- 5
n <- 1000
cov_num <- 3
level_num <- c(2, 3, 5) 
# Set pr to follow two tips:
#(1) length of pr should be sum(level_num);
#(2)sum of probabilities for each margin should be 1.
pr <- c(0.4, 0.6, 0.3, 0.4, 0.3, rep(0.2, times = 5))
omega <- c(0.2, 0.2, rep(0.6 / cov_num, times = cov_num))
# Set block size for stratified randomization
bsize <- 4

## generate a container to contain Diff
DS <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
for(i in 1 : N){
  rtS <- StrPBR.sim(n, cov_num, level_num, pr, bsize)
  DS[ , i] <- rtS$Diff
}

## do some analysis
require(dplyr)

## analyze the overall imbalance
Ana_O <- matrix(NA, nrow = 1, ncol = 3)
rownames(Ana_O) <- c("Str.R")
colnames(Ana_O) <- c("mean", "median", "95%quantile")
tempS <- DS[1, ] %>% abs
Ana_O[1, ] <- c((tempS %>% mean), (tempS %>% median),
                (tempS %>% quantile(0.95)))
## analyze the within-stratum imbalances
tempWS <- DS[2 : 1 + prod(level_num), ] %>% abs
Ana_W <- matrix(NA, nrow = 1, ncol = 3)
rownames(Ana_W) <- c("Str.R")
colnames(Ana_W) <- c("mean", "median", "95%quantile")
Ana_W[1, ] = c((tempWS %>% apply(1, mean) %>% mean),
               (tempWS %>% apply(1, median) %>% mean),
               (tempWS %>% apply(1, mean) %>% quantile(0.95)))

## analyze the marginal imbalance
tempMS <- DS[(1 + prod(level_num) + 1) : (1 + prod(level_num) + sum(level_num)), ] %>% abs
Ana_M <- matrix(NA, nrow = 1, ncol = 3)
rownames(Ana_M) <- c("Str.R");
colnames(Ana_M) <- c("mean", "median", "95%quantile")
Ana_M[1, ] = c((tempMS %>% apply(1, mean) %>% mean),
               (tempMS %>% apply(1, median) %>% mean),
               (tempMS %>% apply(1, mean) %>% quantile(0.95)))

AnaHP <- list(Ana_O, Ana_M, Ana_W)
names(AnaHP) <- c("Overall", "Marginal", "Within-stratum")

AnaHP
# }