AED3_SSR.sim {esDesign} | R Documentation |
Conduct the simulation studies of the Adaptive Enrichment Design (Strategy 3) with Sample Size Re-estimation Procedure based on Futility and Efficacy Stopping Boundaries for the continuous endpoint
Description
The AED3_SSR.sim()
is used to conduct the adaptive enrichment
design with Sample Size Re-estimation, in which futility and efficacy stopping
boundaries are used to guide the adaptive enrichment process. For the
adaptively enriched subgroup, we re-estimate the sample size to maintain an
adequate conditional power meanwhile protect the overall Type I error rate.
Usage
AED3_SSR.sim(N1, rho, alpha, beta, theta, theta0, sigma0, pstar, 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 |
theta |
The sizes of treatment effect in subgroups 1 and 2 with experimental treatment |
theta0 |
The size of treatment effect in standard treatment |
sigma0 |
The known variance of the treatment effect |
pstar |
The |
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
Enrich01 The prevalence of adaptive enrichment of subgroup 1
Enrich02 The prevalence of adaptive enrichment of subgroup 2
Trigger03 The prevalence of early stopping for the situation, in which the treatment effect in subgroup 1 is superiority, while the treatment effect in subgroup 2 is inconclusive
Trigger04 The prevalence of early stopping for the situation, in which the treatment effect in subgroup 2 is superiority, while the treatment effect in subgroup 2 is inconclusive
ESF The probability of early stopping for futility
ESE The probability of early stopping for efficacy
Examples
N <- 310
rho <- 0.5
alpha <- 0.05
beta <- 0.2
theta <- c(0,0)
theta0 <- 0
sigma0 <- 1
pstar <- 0.20
nSim <- 100
Seed <- 6
res <- AED3_SSR.sim(N1 = N, rho = rho, alpha = alpha,
beta = beta, theta = theta, theta0 = theta0,
sigma0 = sigma0, pstar = pstar, nSim = nSim,
Seed = Seed)