esDesign (version 1.0.3)

AED1_SSR.sim: Conduct the simulation studies of the Adaptive Enrichment Design (Strategy 1) with Sample Size Re-estimation Procedure

Description

The AED1_SSR.sim() is used to conduct the simulation study of the Adaptive Enrichment Design (Strategy 1) with Sample Size Re-estimation procedure

Usage

AED1_SSR.sim(
  N1,
  rho,
  alpha,
  beta,
  pstar,
  theta,
  theta0,
  Info,
  K = 2,
  epsilon,
  sigma0,
  nSim,
  Seed
)

Arguments

N1

The sample size used at the first stage

rho

The proportion of subgroup 1 among the overall patients

alpha

The overall Type I error rate

beta

The (1 - Power)

pstar

The (1 - power) of accepting the null hypothesis at the interim analysis.

theta

The sizes of the treatment effect in subgroups 1 and 2 with the experimental arm

theta0

The size of the treatment effect in standard arm

Info

The observation information

K

The number of subgroups. The default value is K = 2

epsilon

The threshold of the difference between the subgroup-specific test statistic

sigma0

The variance of the treatment effect

nSim

The number of simulated studies

Seed

The random seed

Value

A list contains

• nTotal The average expected sample size

• H00 The probability of rejecting the null hypothesis of \(H_{00}\)

• H01 The probability of rejecting the null hypothesis of \(H_{01}\)

• H02 The probability of rejecting the null hypothesis of \(H_{02}\)

• H0 The probabilities of rejecting at least one of the null hypothesis

• ESF The probability of early stopping for futility

• ESE The probability of early stopping for efficacy

• Enrich01 The prevalence of adaptive enrichment of subgroup 1

• Enrich02 The prevalence of adaptive enrichment of subgroup 2

References

• Lin, R., Yang, Z., Yuan, Y. and Yin, G., 2021. Sample size re-estimation in adaptive enrichment design. Contemporary Clinical Trials, 100, p.106216. <doi: 10.1016/j.cct.2020.106216>

Examples

# NOT RUN {
res <- AED1_SSR.sim(
  N1 = 310, rho = 0.5,
  alpha = 0.05, beta = 0.2, pstar = 0.2,
  theta = c(0,0), theta0 = 0, Info = 0.5,
  epsilon = 0.5, sigma0 = 1, nSim = 1000, Seed = 6)
# }