gs_design_ahr: Group sequential design using average hazard ratio under...

In gsDesign2: Group Sequential Design with Non-Constant Effect

Group sequential design using average hazard ratio under non-proportional hazards

Description

Group sequential design using average hazard ratio under non-proportional hazards

Usage

gs_design_ahr(
  enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
  fail_rate = define_fail_rate(duration = c(3, 100), fail_rate = log(2)/c(9, 18), hr =
    c(0.9, 0.6), dropout_rate = 0.001),
  alpha = 0.025,
  beta = 0.1,
  info_frac = NULL,
  analysis_time = 36,
  ratio = 1,
  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),
  h1_spending = TRUE,
  test_upper = TRUE,
  test_lower = TRUE,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  r = 18,
  tol = 1e-06,
  interval = c(0.01, 1000)
)

Arguments

enroll_rate	Enrollment rates.
fail_rate	Failure and dropout rates.
alpha	One-sided Type I error.
beta	Type II error.
info_frac	Targeted information fraction at each analysis.
analysis_time	Minimum time of analysis.
ratio	Experimental:Control randomization ratio (not yet implemented).
binding	Indicator of whether futility bound is binding; default of FALSE is recommended.
upper	Function to compute upper bound.
upar	Parameters passed to upper.
lower	Function to compute lower bound.
lpar	Parameters passed to lower.
h1_spending	Indicator that lower bound to be set by spending under alternate hypothesis (input fail_rate) if spending is used for lower bound.
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.
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.
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).
interval	An interval that is presumed to include the time at which expected event count is equal to targeted event.

Details

To be added.

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(gsDesign)
library(gsDesign2)
library(dplyr)

# Example 1 ----
# call with defaults
gs_design_ahr()

# Example 2 ----
# Single analysis
gs_design_ahr(analysis_time = 40)

# Example 3 ----
# Multiple analysis_time
gs_design_ahr(analysis_time = c(12, 24, 36))

# Example 4 ----
# Specified information fraction

gs_design_ahr(info_frac = c(.25, .75, 1), analysis_time = 36)


# Example 5 ----
# multiple analysis times & info_frac
# driven by times
gs_design_ahr(info_frac = c(.25, .75, 1), analysis_time = c(12, 25, 36))
# driven by info_frac

gs_design_ahr(info_frac = c(1 / 3, .8, 1), analysis_time = c(12, 25, 36))


# Example 6 ----
# 2-sided symmetric design with O'Brien-Fleming spending

gs_design_ahr(
  analysis_time = c(12, 24, 36),
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  h1_spending = FALSE
)

# 2-sided asymmetric design with O'Brien-Fleming upper spending
# Pocock lower spending under H1 (NPH)

gs_design_ahr(
  analysis_time = c(12, 24, 36),
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDPocock, total_spend = 0.1, param = NULL, timing = NULL),
  h1_spending = TRUE
)


# Example 7 ----

gs_design_ahr(
  alpha = 0.0125,
  analysis_time = c(12, 24, 36),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.0125, param = NULL, timing = NULL),
  lower = gs_b,
  lpar = rep(-Inf, 3)
)

gs_design_ahr(
  alpha = 0.0125,
  analysis_time = c(12, 24, 36),
  upper = gs_b,
  upar = gsDesign::gsDesign(
    k = 3, test.type = 1, n.I = c(.25, .75, 1),
    sfu = sfLDOF, sfupar = NULL, alpha = 0.0125
  )$upper$bound,
  lower = gs_b,
  lpar = rep(-Inf, 3)
)

gsDesign2 documentation built on April 3, 2025, 9:39 p.m.