WBR_Sim_Surv: 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 Survival Response

Description

This function simulates a trial using Weighted Balance Ratio design for survival responses.

Usage

WBR_Sim_Surv(
  n,
  mu,
  beta,
  gamma,
  m0 = 40,
  pts.X,
  pts.Z,
  censor.time,
  arrival.rate,
  weight,
  v = 2
)

Arguments

n	a number. The sample size of the simulated data.
mu	a number. The true parameters of treatment effect.
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.
censor.time	a positive number. The upper bound of the uniform censor time in year.
arrival.rate	a positive integer. The arrival rate of patients each year.
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

A list with the following elements:

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.
N.events	Total number of events occured of the trial.
responses	Observed survival responses of patients.
events	Survival status vector of patients(1=event,0=censored)
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 = 0.5                           # treatment effect (log hazard ratio)
beta = c(0.5, -0.5)                # predictive effect and interaction
gamma = c(0.5, 0.5)                # prognostic covariate effects
censor.time = 2                   # maximum censoring time (years)
arrival.rate = 1.5                # arrival rate per year
weight = rep(0.25, 4)             # imbalance weights for overall, margins, and stratum

# 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 survival outcome
result = WBR_Sim_Surv(
  n = n,
  mu = mu,
  beta = beta,
  gamma = gamma,
  pts.X = pts.X,
  pts.Z = pts.Z,
  censor.time = censor.time,
  arrival.rate = arrival.rate,
  weight = weight

)

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