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

Usage

gs_power_combo(
  enroll_rate = define_enroll_rate(duration = 12, rate = 500/12),
  fail_rate = define_fail_rate(duration = c(4, 100), fail_rate = log(2)/15, hr = c(1,
    0.6), dropout_rate = 0.001),
  fh_test = rbind(data.frame(rho = 0, gamma = 0, tau = -1, test = 1, analysis = 1:3,
    analysis_time = c(12, 24, 36)), data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1,
    test = 2:3, analysis = 3, analysis_time = 36)),
  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

enroll_rate: Enrollment rates.
fail_rate: Failure and dropout rates.
fh_test: A data frame to summarize the test in each analysis. See 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: Parameters passed to upper.
lower: Function to compute lower bound.
lpar: Parameters 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 passed to mvtnorm::pmvnorm.

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

enroll_rate <- define_enroll_rate(
  duration = 12,
  rate = 500 / 12
)

fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 15, # median survival 15 month
  hr = c(1, .6),
  dropout_rate = 0.001
)

fh_test <- rbind(
  data.frame(rho = 0, gamma = 0, tau = -1, test = 1, analysis = 1:3, analysis_time = c(12, 24, 36)),
  data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1, test = 2:3, analysis = 3, analysis_time = 36)
)

# Example 1 ----
# Minimal Information Fraction derived bound
# \donttest{
gs_power_combo(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  fh_test = 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)
)
#> $enroll_rate
#> # A tibble: 1 × 3
#>   stratum duration  rate
#>   <chr>      <dbl> <dbl>
#> 1 All           12  41.7
#> 
#> $fail_rate
#> # A tibble: 2 × 5
#>   stratum duration fail_rate dropout_rate    hr
#>   <chr>      <dbl>     <dbl>        <dbl> <dbl>
#> 1 All            4    0.0462        0.001   1  
#> 2 All          100    0.0462        0.001   0.6
#> 
#> $bound
#>   analysis bound  probability probability0         z    nominal p
#> 1        1 upper 6.329275e-08 3.299865e-10  6.175397 3.299865e-10
#> 2        1 lower 3.269613e-04 0.000000e+00 -2.516527 9.940741e-01
#> 3        2 upper 4.260145e-01 2.565830e-03  2.798651 2.565830e-03
#> 4        2 lower 8.468664e-02 0.000000e+00  1.237721 1.079098e-01
#> 5        3 upper 9.015980e-01 2.500822e-02  2.097499 1.797473e-02
#> 6        3 lower 2.000038e-01 0.000000e+00  2.958921 1.543591e-03
#> 
#> $analysis
#>   analysis time        n    event event_frac       ahr
#> 1        1   12 500.0001 107.3943  0.3241690 0.8418858
#> 2        2   24 500.0001 246.2834  0.7434051 0.7164215
#> 3        3   36 500.0001 331.2910  1.0000000 0.6831740
#> 
#> attr(,"class")
#> [1] "non_binding" "combo"       "gs_design"   "list"       
# }