gs_design_rd: 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_rd(
  p_c = tibble(Stratum = "All", Rate = 0.2),
  p_e = tibble(Stratum = "All", Rate = 0.15),
  IF = 1:3/3,
  rd0 = 0,
  alpha = 0.025,
  beta = 0.1,
  ratio = 1,
  stratum_prev = NULL,
  weight = c("un-stratified", "ss", "invar"),
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(0.1), rep(-Inf, 2)),
  test_upper = TRUE,
  test_lower = TRUE,
  info_scale = c(0, 1, 2),
  binding = FALSE,
  r = 18,
  tol = 1e-06,
  h1_spending = FALSE
)

Arguments

p_c	rate at the control group
p_e	rate at the experimental group
IF	statistical information fraction
rd0	treatment effect under super-superiority designs, the default is 0
alpha	One-sided Type I error
beta	Type II error
ratio	Experimental:Control randomization ratio (not yet implemented)
stratum_prev	randomization ratio of different stratum. If it is un-stratified design then NULL. Otherwise it is a tibble containing two columns (Stratum and prevalence).
weight	the weighting scheme for stratified population
upper	Function to compute upper bound
lower	Function to compute lower bound
upar	Parameter passed to upper()
lpar	Parameter passed to lower()
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
binding	indicator of whether futility bound is binding; default of FALSE is recommended
r	Integer, at least 2; default of 18 recommended by Jennison and Turnbull
tol	Tolerance parameter for boundary convergence (on Z-scale)
h1_spending	Indicator that lower bound to be set by spending under alternate hypothesis (input failRates) if spending is used for lower bound

Details

Need to be added

Value

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

Examples

library(tibble)
library(gsDesign)

# ----------------- #
#    example 1      #
#------------------ #
# un-stratified group sequential design
gs_design_rd(
  p_c = tibble(Stratum = "All", Rate = .2),
  p_e = tibble(Stratum = "All", Rate = .15),
  IF = c(0.7, 1),
  rd0 = 0, 
  alpha = .025,                  
  beta = .1,                    
  ratio = 1,
  stratum_prev = NULL,
  weight = "un-stratified",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
  )
  
# ----------------- #
#     example 2     #
# ----------------- #
# stratified group sequential design
gs_design_rd(
  p_c = tibble(Stratum = c("biomarker positive", "biomarker negative"), Rate = c(.2, .25)),
  p_e = tibble(Stratum = c("biomarker positive", "biomarker negative"), Rate = c(.15,.22)),
  IF = c(0.7, 1),
  rd0 = 0, 
  alpha = .025,                  
  beta = .1,                    
  ratio = 1,
  stratum_prev = tibble(Stratum = c("biomarker positive", "biomarker negative"), prevalence = c(.4, .6)),
  weight = "ss",
  upper = gs_spending_bound,lower = gs_b,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lpar = rep(-Inf, 2)
)

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