WBR_Sim: Simulation Function of Weighted Balance Ratio Design for...

In caradpt: Covariate-Adjusted Response-Adaptive Designs for Clinical Trials

Simulation Function of Weighted Balance Ratio Design for Binary and Continuous Response

Description

This function simulates a trial using Weighted Balance Ratio design for binary and continuous responses.

Usage

WBR_Sim(n, mu, beta, gamma, m0 = 40, pts.X, pts.Z, response, weight, v = 2)

Arguments

n	a positive integer. The sample size of the simulated data.
mu	a vector of length 2. The true parameters of treatment.
beta	a vector of length 2. The true parameters of predictive covariate and interaction with treatment.
gamma	a vector of length k. The true parameters of prognostic covariates.
m0	a positive integer. The number of first 2m0 patients will be allocated equally to both treatments.
pts.X	a vector of length n. The vector of patients' binary predictive covariates.
pts.Z	a matrix of n x k. The matrix of patients' binary prognostic covariates.
response	the type of the response. Options are "Binary" or "Cont".
weight	a vector of length 2+k. The weight of balance ratio in overall,margin and stratum levels.
v	a positive value that controls the randomness of allocation probability function.

Value

method	The name of procedure.
sampleSize	The sample size of the trial.
assignment	The randomization sequence.
X1proportion	Average allocation proportion for treatment A when predictive covariate equals the smaller value.
X2proportion	Average allocation proportion for treatment A when predictive covariate equals the larger value.
proportion	Average allocation proportion for treatment A.
failureRate	Proportion of treatment failures (if response = "Binary").
meanResponse	Mean response value (if response = "Cont").

#'

rejectNull

Logical. Indicates whether the treatment effect is statistically significant based on a Wald test.

Examples

set.seed(123)

# Simulation settings
n = 400                              # total number of patients
mu = c(0.8, 0.8)                     # treatment effects (muA, muB)
beta = c(0.8, -0.8)                  # predictive effect and interaction
gamma = c(0.8, 0.8)                  # prognostic covariate effects
weight = rep(0.25, 4)               # weights for imbalance components

# Generate patient covariates
pts.X = sample(c(1, -1), n, replace = TRUE)  # predictive covariate
pts.Z = cbind(
  sample(c(1, -1), n, replace = TRUE),        # prognostic Z1
  sample(c(1, -1), n, replace = TRUE)         # prognostic Z2
)

# Run simulation for continuous response
result = WBR_Sim(
  n = n,
  mu = mu,
  beta = beta,
  gamma = gamma,
  pts.X = pts.X,
  pts.Z = pts.Z,
  response = "Cont",
  weight = weight
)

caradpt documentation built on Aug. 28, 2025, 9:09 a.m.