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

In keaven/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(
  enrollRates = tibble(Stratum = "All", duration = 12, rate = 500/12),
  failRates = tibble(Stratum = "All", duration = c(4, 100), failRate = log(2)/15, hr =
    c(1, 0.6), dropoutRate = 0.001),
  fh_test = rbind(data.frame(rho = 0, gamma = 0, tau = -1, test = 1, Analysis = 1:3,
    analysisTimes = c(12, 24, 36)), data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1,
    test = 2:3, Analysis = 3, analysisTimes = 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

enrollRates	enrollment rates
failRates	failure and dropout rates
fh_test	a data frame to summarize the test in each analysis. Refer examples for its data structure.
ratio	Experimental:Control randomization ratio (not yet implemented)
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
upar	Parameter passed to upper()
lower	Function to compute lower bound
lpar	Parameter passed to lower()
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 transfer to mvtnorm::pmvnorm

Examples

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

enrollRates <- tibble(
  Stratum = "All", 
  duration = 12, 
  rate = 500/12)
  
failRates <- tibble(
  Stratum = "All",
  duration = c(4, 100),
  failRate = log(2) / 15,  # median survival 15 month
  hr = c(1, .6),
  dropoutRate = 0.001)
  
fh_test <- rbind( 
  data.frame(rho = 0, gamma = 0, tau = -1,
             test = 1, Analysis = 1:3, analysisTimes = c(12, 24, 36)),
  data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1,
             test = 2:3, Analysis = 3, analysisTimes = 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          #
# ------------------------ #
## Not run: 
# User defined boundary
gs_design_combo(
  enrollRates,
  failRates,
  fh_test,
  alpha = 0.025, beta = 0.2,
  ratio = 1,
  binding = FALSE,       
  upar = x$upper$bound,
  lpar = x$lower$bound)

## End(Not run)

# -------------------------#
#       example 2          #
# ------------------------ #
# Boundary derived by spending function
gs_design_combo(
  enrollRates,
  failRates,
  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
)

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