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

In keaven/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(
  enrollRates = tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6, 9)),
  failRates = tibble(Stratum = "All", duration = c(3, 100), failRate = log(2)/c(9, 18),
    hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
  ratio = 1,
  alpha = 0.025,
  beta = 0.1,
  IF = NULL,
  analysisTimes = 36,
  binding = FALSE,
  upper = gs_b,
  upar = gsDesign::gsDesign(k = 3, test.type = 1, n.I = c(0.25, 0.75, 1), sfu = sfLDOF,
    sfupar = NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), -Inf, -Inf),
  h1_spending = TRUE,
  test_upper = TRUE,
  test_lower = TRUE,
  info_scale = c(0, 1, 2),
  r = 18,
  tol = 1e-06
)

Arguments

enrollRates	enrollment rates
failRates	failure and dropout rates
ratio	Experimental:Control randomization ratio (not yet implemented)
alpha	One-sided Type I error
beta	Type II error
IF	Targeted information fraction at each analysis
analysisTimes	Minimum time of analysis
binding	indicator of whether futility bound is binding; default of FALSE is recommended
upper	Function to compute upper bound
upar	Parameter passed to upper()
lower	Function to compute lower bound
lpar	Parameter passed to lower()
h1_spending	Indicator that lower bound to be set by spending under alternate hypothesis (input failRates) 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	the information scale for calculation
r	Integer, at least 2; default of 18 recommended by Jennison and Turnbull
tol	Tolerance parameter for boundary convergence (on Z-scale)

Details

Need to be added

Value

a tibble with columns Analysis, Bound, Z, Probability, theta, Time, AHR, Events

Specification

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

Examples

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

# call with defaults
gs_design_ahr()

# Single analysis
gs_design_ahr(analysisTimes = 40)

# Multiple analysisTimes
gs_design_ahr(analysisTimes = c(12, 24, 36))

# Specified information fraction
gs_design_ahr(IF = c(.25, .75, 1), analysisTimes = 36)

# multiple analysis times & IF
# driven by times
gs_design_ahr(IF = c(.25, .75, 1), analysisTimes = c(12, 25, 36))
# driven by IF
gs_design_ahr(IF = c(1/3, .8, 1), analysisTimes = c(12, 25, 36))

# 2-sided symmetric design with O'Brien-Fleming spending
gs_design_ahr(
  analysisTimes = 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(
  analysisTimes = 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)
  

keaven/gsDesign2 documentation built on Oct. 13, 2022, 8:42 p.m.