ZhaoNew_Sim_Surv: Simulation Function for Zhao's New Design for Survival Trial

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

Simulation Function for Zhao's New Design for Survival Trial

Description

This function simulates a clinical trial using Zhao's New design for survival responses.

Usage

ZhaoNew_Sim_Surv(
  n,
  mu,
  beta,
  gamma,
  m0 = 40,
  pts.X,
  pts.Z,
  censor.time,
  arrival.rate,
  omega,
  p = 0.8
)

Arguments

n	a positive integer. The sample size of the simulated data.
mu	a number. 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.Must be binary.
pts.Z	a matrix of n x k. The matrix of patients' binary prognostic covariates.Must be binary.
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.
omega	a vector of length 2+k. The weight of imbalance.
p	a positive value between 0.75 and 0.95. The probability parameter of Efron's biased coin design.

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
m0 = 40                    # initial burn-in sample size
mu = 0.5           # potential means (for continuous or logistic link)
beta = c(1, 1)             # treatment effect and predictive covariate effect
gamma = c(0.1, 0.5)        # prognostic covariate effects
omega = rep(0.25, 4)       # imbalance weights
p = 0.8                    # biased coin probability
censor.time = 2            # maximum censoring time
arrival.rate = 150         # arrival rate of patients
# 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 the simulation (binary response setting)
result = ZhaoNew_Sim_Surv(
  n = n,
  mu = mu,
  beta = beta,
  gamma = gamma,
  m0 = m0,
  pts.X = pts.X,
  pts.Z = pts.Z,
  omega = omega,
  p = p,
  censor.time = censor.time,
  arrival.rate = arrival.rate
)

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