szgaViz {graposas}

Sample size optimization using graphical approach in clinical trial design with two hypotheses

Description

This function computes the optimal design using graphical approach along with the minimum sample size when two hypotheses are considered in a clinical trial.

Usage

szgaViz(
  alpha,
  beta1,
  beta2,
  deltaVec,
  cVec,
  rho,
  wunit,
  initIntvl,
  visualization = TRUE
)

Arguments

alpha

a value of overall type I error rate

beta1

a value of one minus marginal powers for testing H1

beta2

a value of one minus marginal powers for testing H2

deltaVec

a vector of effect sizes for testing H1 nd H2, respectively

cVec

a vector of coefficients. When testing continuous endpoints, these coefficients are exactly one. When testing binary endpoints, the values are roughly one but not exactly one

rho

a value of correlation coefficients between two hypotheses

wunit

a value of initial weight on H1 for grid search and visualization

initIntvl

a vector of lower and upper limits for searching optimal sample size

visualization

a logical value, indicating whether a visualization is needed

Value

a vector of three numbers: the optimal weight on H1 w1, and optimal sample size n1 (based on H1) and n2 (based on H2), where n1 and n2 should be roughly the same

Author(s)

Jiangtao Gou

Fengqing (Zoe) Zhang

References

Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report. Gou, J. (2022). Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints. Biometrical Journal, 64(2), 301-311.

Examples

szgaViz(alpha = 0.05, beta1 = 0.20, beta2 = 0.20,
         deltaVec = c(0.3,0.3), cVec = c(1,1), rho = 0.0,
         wunit= 0.01, initIntvl = c(1,1000),
         visualization = FALSE)