gsDesign2
Group Sequential Design with Non-Constant Effect

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Example Runtime

Example Runtime
In gsDesign2: Group Sequential Design with Non-Constant Effect 

knitr::opts_chunk$set(echo = TRUE)

gs_design_combo.R

# Setup
library(tictoc)
library(dplyr)
library(mvtnorm)
library(gsDesign)
library(gsDesign2)
library(tibble)

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
  )
)

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
)


# Starts the timer and stores the start time and the message on the stack.
tictoc::tic()

# Function run
gs_design_combo(
  enroll_rate,
  fail_rate,
  fh_test,
  alpha = 0.025, beta = 0.2,
  ratio = 1,
  binding = FALSE,
  upar = x$upper$bound,
  lpar = x$lower$bound)

# Notes the current timer and computes elapsed time since the matching call to tic()
tictoc::toc()

# Result: > 5 seconds (10.8 seconds)

# Start the clock!
tictoc::tic()

# Boundary derived by spending function
gs_design_combo(
  enroll_rate,
  fail_rate,
  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
)

# Stop the clock
tictoc::toc()

# Result: > 5 seconds (28.5 seconds)

gs_design_ahr.R

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

tic()
gs_design_ahr()
toc()

tic()
gs_design_ahr(analysis_time = 40)
toc()

tic()
gs_design_ahr(analysis_time = c(12, 24, 36))
toc()

tic()
gs_design_ahr(info_frac = c(.25, .75, 1), analysis_time = 36)
toc()
# Result: 9.2 seconds

tic()
gs_design_ahr(info_frac = c(.25, .75, 1), analysis_time = c(12, 25, 36))
toc()

tic()
gs_design_ahr(info_frac = c(1 / 3, .8, 1), analysis_time = c(12, 25, 36))
toc()
#  Result: 10.4 


tic()
gs_design_ahr(
  analysis_time = 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
)
toc()

tic()
gs_design_ahr(
  analysis_time = 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
)
toc()

gs_design_npe.R

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

# Single analysis
# Lachin book p 71 difference of proportions example
pc <- .28 # Control response rate
pe <- .40 # Experimental response rate
p0 <- (pc + pe) / 2 # Ave response rate under H0

info0 <- 1 / (p0 * (1 - p0) * 4)
info <- 1 / (pc * (1 - pc) * 2 + pe * (1 - pe) * 2)

tic()
gs_design_npe(theta = pe - pc, info = info, info0 = info0)
toc()


tic()
x <- gs_design_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 80,
  info0 = (1:3) * 80,
  upper = gs_b,
  upar = gsDesign::gsDesign(k = 3, sfu = gsDesign::sfLDOF)$upper$bound,
  lower = gs_b,
  lpar = c(-1, 0, 0)
)
x
toc()


tic()
gs_power_npe(
  theta = rep(0, 3),
  info = (x %>% filter(Bound == "Upper"))$info,
  upper = gs_b,
  upar = (x %>% filter(Bound == "Upper"))$Z,
  lower = gs_b,
  lpar = rep(-Inf, 3)
)
toc()



tic()
gs_design_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  info0 = (1:3) * 40,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_b,
  lpar = c(-1, -Inf, -Inf),
  test_upper = c(FALSE, TRUE, TRUE)
)
toc()


tic()
gs_design_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  info0 = (1:3) * 30,
  info_scale = 1,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_b,
  lpar = c(-1, -Inf, -Inf),
  test_upper = c(FALSE, TRUE, TRUE)
)
toc()


tic()
gs_design_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  info0 = (1:3) * 30,
  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::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
)
toc()



tic()
xx <- gs_design_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  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)
)
xx
toc()


tic()
gs_power_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  binding = TRUE,
  upper = gs_b,
  lower = gs_b,
  upar = (xx %>% filter(Bound == "Upper"))$Z,
  lpar = -(xx %>% filter(Bound == "Upper"))$Z
)
toc()

gs_design_rd.R

tic()
gs_design_rd(
  p_c = tibble(stratum = "All", rate = .2),
  p_e = tibble(stratum = "All", rate = .15),
  info_frac = 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))
)
toc()


tic()
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)
  ),
  info_frac = 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)
)
toc()

gs_design_wlr.R

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

# set enrollment rates
enroll_rate <- define_enroll_rate(duration = 12, rate = 500 / 12)
# set failure rates
fail_rate <- tibble(
  stratum = "All",
  duration = c(4, 100),
  fail_rate = log(2) / 15, # median survival 15 month
  hr = c(1, .6),
  dropout_rate = 0.001
)


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
)

tic()
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = function(x, arm0, arm1) {
    wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
  },
  upper = gs_b,
  upar = x$upper$bound,
  lower = gs_b,
  lpar = x$lower$bound,
  analysis_time = c(12, 24, 36)
)
toc()


tic()
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = function(x, arm0, arm1) {
    wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
  },
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
  analysis_time = c(12, 24, 36)
)
toc()

gs_info_ahr.R

library(gsDesign)
library(gsDesign2)

tic()
gs_info_ahr(event = c(30, 40, 50))
toc()
# Result: 13.5 seconds

tic()
gs_info_ahr(analysis_time = c(18, 27, 36))
toc()

tic()
gs_info_ahr(event = c(30, 40, 50), analysis_time = c(16, 19, 26))
toc()
# Result: 5.8 seconds

tic()
gs_info_ahr(event = c(30, 40, 50), analysis_time = c(14, 20, 24))
toc()
# Result: 10.1 seconds

gs_info_rd.R

library(tibble)

tic()
gs_info_rd(
  p_c = tibble(stratum = "All", rate = .15),
  p_e = tibble(stratum = "All", rate = .1),
  n = tibble(stratum = "All", n = c(100, 200, 300), analysis = 1:3),
  rd0 = 0,
  ratio = 1
)
toc()

tic()
gs_info_rd(
  p_c = tibble(stratum = "All", rate = .2),
  p_e = tibble(stratum = "All", rate = .15),
  n = tibble(stratum = "All", n = c(100, 200, 300), analysis = 1:3),
  rd0 = 0.005,
  ratio = 1
)
toc()


tic()
gs_info_rd(
  p_c = tibble(stratum = c("S1", "S2", "S3"), rate = c(.15, .2, .25)),
  p_e = tibble(stratum = c("S1", "S2", "S3"), rate = c(.1, .16, .19)),
  n = tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "ss"
)
toc()



tic()
gs_info_rd(
  p_c = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "invar"
)
toc()



tic()
gs_info_rd(
  p_c = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0.02,
  ratio = 1,
  weight = "ss"
)
toc()


tic()
gs_info_rd(
  p_c = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0.02,
  ratio = 1,
  weight = "invar"
)
toc()


tic()
gs_info_rd(
  p_c = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = tibble(
    stratum = c("S1", "S2", "S3"),
    rd0 = c(0.01, 0.02, 0.03)
  ),
  ratio = 1,
  weight = "invar"
)
toc()

gs_info_wlr.R

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

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


event <- c(30, 40, 50)
analysis_time <- c(10, 24, 30)


tic()
gs_info_wlr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  event = event, analysis_time = analysis_time
)
toc()

gs_power_ahr.R

tic()
gs_power_ahr()
toc()
# Result: 14.2 seconds

tic()
gs_power_ahr(
  analysis_time = c(12, 24, 36),
  event = NULL,
  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)
)
toc()


tic()
gs_power_ahr(
  analysis_time = NULL,
  event = c(20, 50, 70),
  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)
)
toc()
# Result: 14.3 seconds


tic()
gs_power_ahr(
  analysis_time = c(12, 24, 36),
  event = c(30, 40, 50),
  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)
)
toc()
# Result: 7.1 seconds

gs_power_combo.R

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


enroll_rate <- tibble(
  stratum = "All",
  duration = 12,
  rate = 500 / 12
)
fail_rate <- tibble(
  stratum = "All",
  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)
)


tic()
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)
)
toc()
# Result: 8.5 seconds

gs_power_npe.R

tic()
gs_power_npe(theta = 0) %>% filter(Bound == "Upper")
toc()


tic()
gs_power_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  upper = gs_b,
  upar = gsDesign::gsDesign(k = 3, sfu = gsDesign::sfLDOF)$upper$bound,
  lower = gs_b,
  lpar = c(-1, 0, 0)
)
toc()


tic()
gs_power_npe(
  theta = rep(0, 3),
  info = (1:3) * 40,
  upar = gsDesign::gsDesign(k = 3, sfu = gsDesign::sfLDOF)$upper$bound,
  lpar = rep(-Inf, 3)
) %>%
  filter(Bound == "Upper")
toc()


tic()
gs_power_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  upper = gs_b,
  upar = c(Inf, 3, 2),
  lower = gs_b,
  lpar = c(qnorm(.1), -Inf, -Inf)
)
toc()



tic()
gs_power_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  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::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
)
toc()



tic()
gs_power_npe(
  theta = c(.15, .25, .35),
  info = (1:3) * 40,
  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::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
)
toc()





x <- gs_power_npe(
  theta = rep(0, 3),
  info = (1:3) * 40,
  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)
)

tic()
# Re-use these bounds under alternate hypothesis
# Always use binding = TRUE for power calculations
gs_power_npe(
  theta = c(.1, .2, .3),
  info = (1:3) * 40,
  binding = TRUE,
  upar = (x %>% filter(Bound == "Upper"))$Z,
  lpar = -(x %>% filter(Bound == "Upper"))$Z
)
toc()

gs_power_rd.R

tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = "All",
    rate = .2
  ),
  p_e = tibble::tibble(
    stratum = "All",
    rate = .15
  ),
  n = tibble::tibble(
    stratum = "All",
    n = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0,
  ratio = 1,
  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))
)
toc()





tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = "All",
    rate = .2
  ),
  p_e = tibble::tibble(
    stratum = "All",
    rate = .15
  ),
  n = tibble::tibble(
    stratum = "All",
    n = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0.005,
  ratio = 1,
  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))
)
toc()





tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = "All",
    rate = .2
  ),
  p_e = tibble::tibble(
    stratum = "All",
    rate = .15
  ),
  n = tibble::tibble(
    stratum = "All",
    n = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0.005,
  ratio = 1,
  upper = gs_spending_bound,
  lower = gs_b,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lpar = c(qnorm(.1), rep(-Inf, 2))
)
toc()






tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "ss",
  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))
)
toc()


tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "invar",
  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))
)
toc()



tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0.02,
  ratio = 1,
  weight = "ss",
  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))
)
toc()


tic()
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0.03,
  ratio = 1,
  weight = "invar",
  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))
)
toc()

gs_power_wlr.R

library(tibble)
library(gsDesign)
library(gsDesign2)

# set enrollment rates
enroll_rate <- define_enroll_rate(duration = 12, rate = 500 / 12)
# set failure rates
fail_rate <- tibble(
  stratum = "All",
  duration = c(4, 100),
  fail_rate = log(2) / 15, # median survival 15 month
  hr = c(1, .6),
  dropout_rate = 0.001
)
# set the targeted number of events and analysis time
target_events <- c(30, 40, 50)
target_analysisTime <- c(10, 24, 30)


tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = NULL,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)
toc()
# Results: 14.3 seconds



tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = NULL,
  analysis_time = target_analysisTime,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)
toc()



tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = target_analysisTime,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)
toc()


tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = NULL,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2)
)
toc()
# Result: 15 seconds


tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = NULL,
  analysis_time = target_analysisTime,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2)
)
toc()



tic()
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = target_analysisTime,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2)
)
toc()

gs_spending_combo.R

par <- list(sf = gsDesign::sfLDOF, total_spend = 0.025)

tic()
gs_spending_combo(par, info = 1:3 / 3)
toc()

par <- list(sf = gsDesign::sfLDOF, total_spend = 0.2)
tic()
gs_spending_combo(par, info = 1:3 / 3)
toc()

summary.R 

# Enrollment rate
enroll_rate <- tibble::tibble(
  stratum = "All",
  duration = 18,
  rate = 20
)

# Failure rates
fail_rate <- tibble::tibble(
  stratum = "All",
  duration = c(4, 100),
  fail_rate = log(2) / 12,
  hr = c(1, .6),
  dropout_rate = .001
)

# Study duration in months
study_duration <- 36

# Experimental / Control randomization ratio
ratio <- 1

# 1-sided Type I error
alpha <- 0.025
# Type II error (1 - power)
beta <- 0.1

# AHR ----

tic()

# under fixed power
fixed_design(
  "ahr",
  alpha = alpha,
  power = 1 - beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  study_duration = study_duration,
  ratio = ratio
) %>% summary()

toc()

# FH ----
# under fixed power

tic()

fixed_design(
  "fh",
  alpha = alpha,
  power = 1 - beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  study_duration = study_duration,
  ratio = ratio
) %>% summary()

toc()


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gsDesign2 documentation built on April 3, 2025, 9:39 p.m.

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