gs_design_combo: Group sequential design using MaxCombo test under...

In gsDesign2: Group Sequential Design with Non-Constant Effect

Group sequential design using MaxCombo test under non-proportional hazards

Description

Group sequential design using MaxCombo test under non-proportional hazards

Usage

gs_design_combo(
  enroll_rate = define_enroll_rate(duration = 12, rate = 500/12),
  fail_rate = define_fail_rate(duration = c(4, 100), fail_rate = log(2)/15, hr = c(1,
    0.6), dropout_rate = 0.001),
  fh_test = rbind(data.frame(rho = 0, gamma = 0, tau = -1, test = 1, analysis = 1:3,
    analysis_time = c(12, 24, 36)), data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1,
    test = 2:3, analysis = 3, analysis_time = 36)),
  ratio = 1,
  alpha = 0.025,
  beta = 0.2,
  binding = FALSE,
  upper = gs_b,
  upar = c(3, 2, 1),
  lower = gs_b,
  lpar = c(-1, 0, 1),
  algorithm = mvtnorm::GenzBretz(maxpts = 1e+05, abseps = 1e-05),
  n_upper_bound = 1000,
  ...
)

Arguments

enroll_rate	Enrollment rates defined by define_enroll_rate().
fail_rate	Failure and dropout rates defined by define_fail_rate().
fh_test	A data frame to summarize the test in each analysis. See examples for its data structure.
ratio	Experimental:Control randomization ratio.
alpha	One-sided Type I error.
beta	Type II error.
binding	Indicator of whether futility bound is binding; default of FALSE is recommended.
upper	Function to compute upper bound. gs_spending_bound(): alpha-spending efficacy bounds. gs_b(): fixed efficacy bounds.
upar	Parameters passed to upper. If upper = gs_b, then upar is a numerical vector specifying the fixed efficacy bounds per analysis. If upper = gs_spending_bound, then upar is a list including sf for the spending function family. total_spend for total alpha spend. param for the parameter of the spending function. timing specifies spending time if different from information-based spending; see details.
lower	Function to compute lower bound, which can be set up similarly as upper. See this vignette.
lpar	Parameters passed to lower, which can be set up similarly as upar.
algorithm	an object of class GenzBretz, Miwa or TVPACK specifying both the algorithm to be used as well as the associated hyper parameters.
n_upper_bound	A numeric value of upper limit of sample size.
...	Additional parameters passed to mvtnorm::pmvnorm.

Value

A list with input parameters, enrollment rate, analysis, and bound.

Examples

# The example is slow to run
library(dplyr)
library(mvtnorm)
library(gsDesign)

enroll_rate <- define_enroll_rate(
  duration = 12,
  rate = 500 / 12
)

fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 15, # median survival 15 month
  hr = c(1, .6),
  dropout_rate = 0.001
)

fh_test <- rbind(
  data.frame(
    rho = 0, gamma = 0, tau = -1,
    test = 1, analysis = 1:3, analysis_time = c(12, 24, 36)
  ),
  data.frame(
    rho = c(0, 0.5), gamma = 0.5, tau = -1,
    test = 2:3, analysis = 3, analysis_time = 36
  )
)

x <- gsSurv(
  k = 3,
  test.type = 4,
  alpha = 0.025,
  beta = 0.2,
  astar = 0,
  timing = 1,
  sfu = sfLDOF,
  sfupar = 0,
  sfl = sfLDOF,
  sflpar = 0,
  lambdaC = 0.1,
  hr = 0.6,
  hr0 = 1,
  eta = 0.01,
  gamma = 10,
  R = 12,
  S = NULL,
  T = 36,
  minfup = 24,
  ratio = 1
)

# Example 1 ----
# User-defined boundary

gs_design_combo(
  enroll_rate,
  fail_rate,
  fh_test,
  alpha = 0.025, beta = 0.2,
  ratio = 1,
  binding = FALSE,
  upar = x$upper$bound,
  lpar = x$lower$bound
)

# Example 2 ----

# Boundary derived by spending function
gs_design_combo(
  enroll_rate,
  fail_rate,
  fh_test,
  alpha = 0.025,
  beta = 0.2,
  ratio = 1,
  binding = FALSE,
  upper = gs_spending_combo,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), # alpha spending
  lower = gs_spending_combo,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2), # beta spending
)

gsDesign2 documentation built on June 8, 2025, 12:26 p.m.