gs_design_wlr: Group sequential design using weighted log-rank test under...

In gsDesign2: Group Sequential Design with Non-Constant Effect

Group sequential design using weighted log-rank test under non-proportional hazards

Description

Group sequential design using weighted log-rank test under non-proportional hazards

Usage

gs_design_wlr(
  enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
  fail_rate = tibble(stratum = "All", duration = c(3, 100), fail_rate = log(2)/c(9, 18),
    hr = c(0.9, 0.6), dropout_rate = rep(0.001, 2)),
  weight = "logrank",
  approx = "asymptotic",
  alpha = 0.025,
  beta = 0.1,
  ratio = 1,
  info_frac = NULL,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  analysis_time = 36,
  binding = FALSE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = alpha),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = beta),
  test_upper = TRUE,
  test_lower = TRUE,
  h1_spending = TRUE,
  r = 18,
  tol = 1e-06,
  interval = c(0.01, 1000)
)

Arguments

enroll_rate	Enrollment rates defined by define_enroll_rate().
fail_rate	Failure and dropout rates defined by define_fail_rate().
weight	Weight of weighted log rank test: "logrank" = regular logrank test. list(method = "fh", param = list(rho = ..., gamma = ...)) = Fleming-Harrington weighting functions. list(method = "mb", param = list(tau = ..., w_max = ...)) = Magirr and Burman weighting functions.
approx	Approximate estimation method for Z statistics. "event_driven" = only work under proportional hazard model with log rank test. "asymptotic".
alpha	One-sided Type I error.
beta	Type II error.
ratio	Experimental:Control randomization ratio.
info_frac	Targeted information fraction for analyses. See details.
info_scale	Information scale for calculation. Options are: "h0_h1_info" (default): variance under both null and alternative hypotheses is used. "h0_info": variance under null hypothesis is used. "h1_info": variance under alternative hypothesis is used.
analysis_time	Targeted calendar timing of analyses. See details.
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.
test_upper	Indicator of which analyses should include an upper (efficacy) bound; single value of TRUE (default) indicates all analyses; otherwise, a logical vector of the same length as info should indicate which analyses will have an efficacy bound.
test_lower	Indicator of which analyses should include an lower bound; single value of TRUE (default) indicates all analyses; single value FALSE indicated no lower bound; otherwise, a logical vector of the same length as info should indicate which analyses will have a lower bound.
h1_spending	Indicator that lower bound to be set by spending under alternate hypothesis (input fail_rate) if spending is used for lower bound. If this is FALSE, then the lower bound spending is under the null hypothesis. This is for two-sided symmetric or asymmetric testing under the null hypothesis; See this vignette.
r	Integer value controlling grid for numerical integration as in Jennison and Turnbull (2000); default is 18, range is 1 to 80. Larger values provide larger number of grid points and greater accuracy. Normally, r will not be changed by the user.
tol	Tolerance parameter for boundary convergence (on Z-scale); normally not changed by the user.
interval	An interval presumed to include the times at which expected event count is equal to targeted event. Normally, this can be ignored by the user as it is set to c(.01, 1000).

Value

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

Specification

The contents of this section are shown in PDF user manual only.

Examples

library(dplyr)
library(mvtnorm)
library(gsDesign)
library(gsDesign2)

# set enrollment rates
enroll_rate <- define_enroll_rate(duration = 12, rate = 1)

# set failure rates
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
)

# Example 1 ----
# Information fraction driven design
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = list(method = "mb", param = list(tau = Inf, w_max = 2)),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
  analysis_time = 36,
  info_frac = c(0.6, 1)
)

# Example 2 ----
# Calendar time driven design
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = list(method = "mb", param = list(tau = Inf, w_max = 2)),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
  analysis_time = c(24, 36),
  info_frac = NULL
)

# Example 3 ----
# Both calendar time and information fraction driven design
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = list(method = "mb", param = list(tau = Inf, w_max = 2)),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
  analysis_time = c(24, 36),
  info_frac = c(0.6, 1)
)

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