Group sequential design power using MaxCombo test under non-proportional hazards

Usage

gs_power_combo(
  enrollRates,
  failRates,
  fh_test,
  ratio = 1,
  binding = FALSE,
  upper = gs_b,
  upar = c(3, 2, 1),
  lower = gs_b,
  lpar = c(-1, 0, 1),
  algorithm = GenzBretz(maxpts = 1e+05, abseps = 1e-05),
  ...
)

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)
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.
...: additional parameters transfer to mvtnorm::pmvnorm

Specification

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

Examples

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

enrollRates <- tibble::tibble(Stratum = "All", duration = 12, rate = 500/12)

failRates <- tibble::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)
)

# User defined bound
gs_power_combo(enrollRates, failRates, fh_test, upar = c(3,2,1), lpar = c(-1, 0, 1))
#>   Analysis Bound Time        N   Events  Z Probability Probability_Null
#> 1        1 Upper   12 500.0001 107.3943  3  0.01750147      0.001349898
#> 3        2 Upper   24 500.0001 246.2834  2  0.72610227      0.023307604
#> 5        3 Upper   36 500.0001 331.2910  1  0.96742568      0.195585465
#> 2        1 Lower   12 500.0001 107.3943 -1  0.02926711               NA
#> 4        2 Lower   24 500.0001 246.2834  0  0.03142375               NA
#> 6        3 Lower   36 500.0001 331.2910  1  0.03257732               NA

# Minimal Information Fraction derived bound
gs_power_combo(enrollRates, failRates, fh_test,
               upper = gs_spending_combo,
               upar  = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
               lower = gs_spending_combo,
               lpar  = list(sf = gsDesign::sfLDOF, total_spend = 0.2))
#>   Analysis Bound Time        N   Events         Z  Probability Probability_Null
#> 1        1 Upper   12 500.0001 107.3943  6.175397 6.329275e-08     3.299865e-10
#> 3        2 Upper   24 500.0001 246.2834  2.798651 4.260145e-01     2.565830e-03
#> 5        3 Upper   36 500.0001 331.2910  2.097932 9.015802e-01     2.495060e-02
#> 2        1 Lower   12 500.0001 107.3943 -2.516527 3.269613e-04               NA
#> 4        2 Lower   24 500.0001 246.2834  1.237721 8.468664e-02               NA
#> 6        3 Lower   36 500.0001 331.2910  2.097932 9.842290e-02               NA