optsamplesize_iHp: Compute the optimal sample size for the improved trimmed...

In itrimhoch: Improved Trimmed Weighted Hochberg Procedures and Sample Size Optimization

Compute the optimal sample size for the improved trimmed weighted Hochberg procedure

Description

Compute the optimal sample size for the improved trimmed weighted Hochberg procedure

Usage

optsamplesize_iHp(
  alpha,
  k,
  betavec,
  deltavec,
  rho,
  ninterval = c(2, 2000),
  alphalist = seq(from = 0, to = alpha, by = 0.005)
)

Arguments

alpha	the significance level
k	a pre-specified constant in the improved trimmed weighted Hochberg procedure
betavec	a numeric vector of two values, including one minus the desired power for rejecting H1 and one minus the desired power for rejecting H2
deltavec	a numeric vector of two values representing the effect sizes for the two hypotheses
rho	the correlation coefficient between two test statistics
ninterval	a vector containing the end-points of the interval to be searched for optimal sample size
alphalist	a vector of discrete alpha values

Value

the overall optimal sample size for the improved trimmed weighted Hochberg procedure

Author(s)

Jiangtao Gou

Fengqing Zhang

References

Gou, J., Chang, Y., Li, T., and Zhang, F. (2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.

Examples

rrr <- 2 # Allocation ratio
alpha <- 0.025
k <- 2/3
ninterval <- c(2, 1000)
betavec <- c(0.05, 0.15)
rho <- 0.3
psivec <- c(0.67, 0.73)
thetavec <- log(psivec)
deltavec <- (-thetavec)*sqrt(rrr)/(1+rrr)
result <- optsamplesize_iHp(alpha = alpha, k = k,
betavec = betavec, deltavec = deltavec,
rho = rho, ninterval = ninterval)
result$nopt

itrimhoch documentation built on June 8, 2025, 11:54 a.m.