SSR.sim {esDesign}

Conduct the simulation studies using SSR

Description

The SSR.sim() is used to implement the simulation studies based on the Sample Size Re-estimation Procedure.

Usage

SSR.sim(N, rho, alpha, beta, theta, theta0, sigma0, pstar, nSim, Seed)

Arguments

N

The sample size used at the first stage. Note that this N is not the initial total sample size calculated using the standard design

rho

The proportion of subgroup 1

alpha

The overall Type I error rate

beta

The (1 - Power)

theta

The sizes of treatment effects for subgroups 1 and 2 in the experimental arm

theta0

The size of treatment effect in the control arm

sigma0

The variance of the treatment effect

pstar

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

nSim

The number of simulated studies

Seed

The random seed

Value

A list contains

• nTotal The average total sample size used in SSR

• H0 The power of SSR under the specific trial design. Here, the power is defined as the probability of rejecting the null hypothesis

• ESF The percentage of early stopping for futility

• ESE The percentage of early stopping for efficacy

References

• Proschan MA, Hunsberger SA. Designed extension of studies based on conditional power. Biometrics 1995:1315-1324. <doi:10.2307/2533262>

Examples

N <- 310
rho <- 0.5
alpha <- 0.05
beta <- 0.2
pstar <- 0.2
theta <- c(0.2,0)
theta0 <- 0
sigma0 <- 1.0
nSim <- 1000
Seed <- 6
res <- SSR.sim(N = N, rho = rho, alpha = alpha, beta = beta, theta = theta,
        theta0 = theta0, sigma0 = sigma0, pstar = pstar,
        nSim = nSim, Seed = Seed)