HuHuCAR {carat}

Hu and Hu's General Covariate-Adaptive Randomization

Description

Allocates patients to one of two treatments using Hu and Hu's general covariate-adaptive randomization proposed by Hu Y, Hu F (2012) <doi:10.1214/12-AOS983>.

Usage

HuHuCAR(data, omega = NULL, p = 0.85)

Arguments

data

a data frame. A row of the dataframe corresponds to the covariate profile of a patient.

omega

a vector of weights at the overall, within-stratum, and within-covariate-margin levels. It is required that at least one element is larger than 0. If omega = NULL (default), the overall, within-stratum, and within-covariate-margin imbalances are weighted with porportions 0.2, 0.3, and 0.5/cov_num for each covariate-margin, respectively, where cov_num is the number of covariates of interest.

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 general covariate-adaptive randomization procedure is as follows:

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

(2) Suppose that 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 overall, within-covariate-margin, and within-stratum differences between the two treatments would be

D_j^{(1)}=D_{j-1}+1,

D_j^{(1)}(i;k_i^*)=D_{j-1}(i,k_i^*)+1,

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

for margin (i;k_i^*) and stratum (k_1^*,\ldots,k_I^*). Similarly, the potential differences at the overall, within-covariate-margin, and within-stratum levels would be obtained if the jth patient were assigned to treatment 2;

(4) An imbalance measure is defined by

Imb_j^{(l)}=\omega_o[D_j^{(l)}]^2+\sum_{i=1}^{I}\omega_{m,i}[D_j^{(l)}(i;k_i^*)]^2+\omega_s[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 covariate 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 Hu and Hu (2012).

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.

weight

a vector giving the weights imposed on each covariate.

framework

the framework of the used randomization procedure: stratified randomization, or model-based method.

data

the data frame.

References

Hu Y, Hu F. Asymptotic properties of covariate-adaptive randomization[J]. The Annals of Statistics, 2012, 40(3): 1794-1815.

Ma W, Ye X, Tu F, Hu F. carat: Covariate-Adaptive Randomization for Clinical Trials[J]. Journal of Statistical Software, 2023, 107(2): 1-47.

See Also

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

Examples

# a simple use
## Real Data
## create 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)
omega <- c(1, 2, rep(1, 3))
Res <- HuHuCAR(data = df, omega)
## 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)
omega <- rep(0.2, times = 5)
Res.sim <- HuHuCAR.sim(n = 100, cov_num, level_num, pr, omega)
## view the output
Res.sim

## view the detials of difference
Res.sim$Diff


N <- 100 # << adjust according to your CPU
n <- 1000
cov_num <- 3
level_num <- c(2, 3, 5) # << adjust to your CPU and the length should correspond to cov_num
# 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 omega0 = omegaS = 0
omegaP <- c(0, 0, rep(1 / cov_num, times = cov_num))

## generate a container to contain Diff
DH <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
DP <- 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)
  resultP <- HuHuCAR.sim(n, cov_num, level_num, pr, omegaP)
  DH[ , i] <- result$Diff; DP[ , i] <- resultP$Diff
}

## do some analysis
require(dplyr)

## analyze the overall imbalance
Ana_O <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_O) <- c("NEW", "PS")
colnames(Ana_O) <- c("mean", "median", "95%quantile")
temp <- DH[1, ] %>% abs
tempP <- DP[1, ] %>% abs
Ana_O[1, ] <- c((temp %>% mean), (temp %>% median),
                (temp %>% quantile(0.95)))
Ana_O[2, ] <- c((tempP %>% mean), (tempP %>% median),
                (tempP %>% quantile(0.95)))
## analyze the within-stratum imbalances
tempW <- DH[2 : (1 + prod(level_num)), ] %>% abs
tempWP <- DP[2 : 1 + prod(level_num), ] %>% abs
Ana_W <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_W) <- c("NEW", "PS")
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((tempWP %>% apply(1, mean) %>% mean),
               (tempWP %>% apply(1, median) %>% mean),
               (tempWP %>% 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
tempMP <- DP[(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", "PS"); 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((tempMP %>% apply(1, mean) %>% mean),
               (tempMP %>% apply(1, median) %>% mean),
               (tempMP %>% apply(1, mean) %>% quantile(0.95)))

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

AnaHP