StrBCD {carat} R Documentation

## Shao's Method in the Two-Arms Case

### Description

Allocates patients to one of the two treatments using Shao's method proposed by Shao J, Yu X, Zhong B (2010) <doi:10.1093/biomet/asq014>.

### Usage

StrBCD(data, p = 0.85)


### Arguments

 data a data frame. A row of the dataframe corresponds to the covariate profile of a patient. p the biased coin probability. p should be larger than 1/2 and less than 1. The default is 0.85.

### Details

Consider I covariates and m_i levels for the ith covariate, i=1,\ldots,I. T_j is the assignment of the jth patient and Z_j = (k_1,\dots,k_I) indicates the covariate profile of this patient, j=1,\ldots,n. For convenience, (k_1,\dots,k_I) and (i;k_i) denote the stratum and margin, respectively. D_j(.) is the difference between the numbers of patients assigned to treatment 1 and treatment 2 at the corresponding levels after j patients have been assigned. The stratified biased coin design is as follows:

(1) The first patient is assigned to treatment 1 with probability 1/2;

(2) Suppose j-1 patients have been assigned (1<j\le n) and the jth patient falls within (k_1^*,\dots,k_I^*);

(3) If the jth patient were assigned to treatment 1, then the potential within-stratum difference between the two treatments would be

D_j^{(1)}(k_1^*,\dots,k_I^*)=D_j(k_1^*,\dots,k_I^*)+1

for stratum (k_1^*,\ldots,k_I^*). Similarly, the potential difference would be obtained in the same way if the jth patient were assigned to treatment 2;

(4) An imbalance measure is defined by

Imb_j^{(l)}=[D_j^{(l)}(k_1^*,\dots,k_I^*)]^2,l=1,2;

(5) Conditional on the assignments of the first (j-1) patients as well as the covariates'profiles of the first j patients, assign the jth patient to treatment 1 with probability

P(T_j=1|Z_j,T_1,\dots,T_{j-1})=q

for Imb_j^{(1)}>Imb_j^{(2)},

P(T_j=1|Z_j,T_1,\dots,T_{j-1})=p

for Imb_j^{(1)}<Imb_j^{(2)}, and

P(T_j=1|Z_j,T_1,\dots,T_{j-1})=0.5

for Imb_j^{(1)}=Imb_j^{(2)}.

Details of the procedure can be found in Shao J, Yu X, Zhong B (2010).

### Value

It returns an object of class "carandom".

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

 datanumeric a bool indicating whether the data is a numeric data frame. covariates a character string giving the name(s) of the included covariates. strt_num the number of strata. cov_num the number of covariates. level_num a vector of level numbers for each covariate. n the number of patients. Cov_Assig a (cov_num + 1) * n matrix containing covariate profiles for all patients and the corresponding assignments. The ith column represents the ith patient. The first cov_num rows include patients' covariate profiles, and the last row contains the assignments. assignments the randomization sequence. 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 within-covariate-margin levels. method a character string describing the randomization procedure to be used. Data Type a character string giving the data type, Real or Simulated. framework the framework of the used randomization procedure: stratified randomization, or model-based method. data the data frame.

### References

Shao J, Yu X, Zhong B. A theory for testing hypotheses under covariate-adaptive randomization[J]. Biometrika, 2010, 97(2): 347-360.

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

### Examples

# a simple use
## Real Data
## creat a dataframe
df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)),
"age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE),
"jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE),
stringsAsFactors = TRUE)
Res <- StrBCD(data = df)
## view the output
Res

## view all patients' profile and assignments
Res$Cov_Assig ## Simulated Data cov_num = 3 level_num = c(2, 3, 3) pr = c(0.4, 0.6, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3) Res.sim <- StrBCD.sim(n = 1000, cov_num, level_num, pr) ## view the output Res.sim ## view the detials of difference Res.sim$Diff

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))

## generate a container to contain Diff
DH <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
DS <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
for(i in 1 : N){
result <- HuHuCAR.sim(n, cov_num, level_num, pr, omega)
resultS <- StrBCD.sim(n, cov_num, level_num, pr)
DH[ , i] <- result$Diff; DS[ , i] <- resultS$Diff
}

## do some analysis
require(dplyr)

## analyze the overall imbalance
Ana_O <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_O) <- c("NEW", "Shao")
colnames(Ana_O) <- c("mean", "median", "95%quantile")
temp <- DH[1, ] %>% abs
tempS <- DS[1, ] %>% abs
Ana_O[1, ] <- c((temp %>% mean), (temp %>% median),
(temp %>% quantile(0.95)))
Ana_O[2, ] <- c((tempS %>% mean), (tempS %>% median),
(tempS %>% quantile(0.95)))

## analyze the within-stratum imbalances
tempW <- DH[2 : (1 + prod(level_num)), ] %>% abs
tempWS <- DS[2 : 1 + prod(level_num), ] %>% abs
Ana_W <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_W) <- c("NEW", "Shao")
colnames(Ana_W) <- c("mean", "median", "95%quantile")
Ana_W[1, ] = c((tempW %>% apply(1, mean) %>% mean),
(tempW %>% apply(1, median) %>% mean),
(tempW %>% apply(1, mean) %>% quantile(0.95)))
Ana_W[2, ] = c((tempWS %>% apply(1, mean) %>% mean),
(tempWS %>% apply(1, median) %>% mean),
(tempWS %>% apply(1, mean) %>% quantile(0.95)))

## analyze the marginal imbalance
tempM <- DH[(1 + prod(level_num) + 1) :
(1 + prod(level_num) + sum(level_num)), ] %>% abs
tempMS <- DS[(1 + prod(level_num) + 1) :
(1 + prod(level_num) + sum(level_num)), ] %>% abs
Ana_M <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_M) <- c("NEW", "Shao")
colnames(Ana_M) <- c("mean", "median", "95%quantile")
Ana_M[1, ] = c((tempM %>% apply(1, mean) %>% mean),
(tempM %>% apply(1, median) %>% mean),
(tempM %>% apply(1, mean) %>% quantile(0.95)))
Ana_M[2, ] = 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



[Package carat version 2.0.2 Index]