sim_gs_n: Simulate group sequential designs with fixed sample size

In Merck/simtrial: Clinical Trial Simulation

Simulate group sequential designs with fixed sample size

Description

This function uses the option "stop" for the error-handling behavior of the foreach loop. This will cause the entire function to stop when errors are encountered and return the first error encountered instead of returning errors for each individual simulation.

Usage

sim_gs_n(
  n_sim = 1000,
  sample_size = 500,
  stratum = data.frame(stratum = "All", p = 1),
  enroll_rate = data.frame(duration = c(2, 2, 10), rate = c(3, 6, 9)),
  fail_rate = data.frame(stratum = "All", duration = c(3, 100), fail_rate = log(2)/c(9,
    18), hr = c(0.9, 0.6), dropout_rate = rep(0.001, 2)),
  block = rep(c("experimental", "control"), 2),
  test = wlr,
  cut = NULL,
  ...
)

Arguments

n_sim	Number of simulations to perform.
sample_size	Total sample size per simulation.
stratum	A data frame with stratum specified in stratum, probability (incidence) of each stratum in p.
enroll_rate	Piecewise constant enrollment rates by time period. Note that these are overall population enrollment rates and the stratum argument controls the random distribution between stratum.
fail_rate	Piecewise constant control group failure rates, hazard ratio for experimental vs. control, and dropout rates by stratum and time period.
block	As in sim_pw_surv(). Vector of treatments to be included in each block.
test	One or more test functions such as wlr(), rmst(), or milestone() (maxcombo() can only be applied by itself). If a single test function is provided, it will be applied at each cut. Alternatively a list of functions created by create_test(). The list form is experimental and currently limited. It only accepts one test per cutting (in the future multiple tests may be accepted), and all the tests must consistently return the same exact results (again this may be more flexible in the future). Importantly, note that the simulated data set is always passed as the first positional argument to each test function provided.
cut	A list of cutting functions created by create_cut(), see examples.
...	Arguments passed to the test function(s) provided by the argument test.

Details

WARNING: This experimental function is a work-in-progress. The function arguments will change as we add additional features.

Value

A data frame summarizing the simulation ID, analysis date, z statistics or p-values.

Examples

library(gsDesign2)

# Parameters for enrollment
enroll_rampup_duration <- 4 # Duration for enrollment ramp up
enroll_duration <- 16 # Total enrollment duration
enroll_rate <- define_enroll_rate(
  duration = c(
    enroll_rampup_duration,
    enroll_duration - enroll_rampup_duration
  ),
  rate = c(10, 30)
)

# Parameters for treatment effect
delay_effect_duration <- 3 # Delay treatment effect in months
median_ctrl <- 9 # Survival median of the control arm
median_exp <- c(9, 14) # Survival median of the experimental arm
dropout_rate <- 0.001
fail_rate <- define_fail_rate(
  duration = c(delay_effect_duration, 100),
  fail_rate = log(2) / median_ctrl,
  hr = median_ctrl / median_exp,
  dropout_rate = dropout_rate
)

# Other related parameters
alpha <- 0.025 # Type I error
beta <- 0.1 # Type II error
ratio <- 1 # Randomization ratio (experimental:control)

# Define cuttings of 2 IAs and 1 FA
# IA1
# The 1st interim analysis will occur at the later of the following 3 conditions:
# - At least 20 months have passed since the start of the study.
# - At least 100 events have occurred.
# - At least 20 months have elapsed after enrolling 200/400 subjects, with a
#   minimum of 20 months follow-up.
# However, if events accumulation is slow, we will wait for a maximum of 24 months.
ia1_cut <- create_cut(
  planned_calendar_time = 20,
  target_event_overall = 100,
  max_extension_for_target_event = 24,
  min_n_overall = 200,
  min_followup = 20
)

# IA2
# The 2nd interim analysis will occur at the later of the following 3 conditions:
# - At least 32 months have passed since the start of the study.
# - At least 250 events have occurred.
# - At least 10 months after IA1.
# However, if events accumulation is slow, we will wait for a maximum of 34 months.
ia2_cut <- create_cut(
  planned_calendar_time = 32,
  target_event_overall = 200,
  max_extension_for_target_event = 34,
  min_time_after_previous_analysis = 10
)

# FA
# The final analysis will occur at the later of the following 2 conditions:
# - At least 45 months have passed since the start of the study.
# - At least 300 events have occurred.
fa_cut <- create_cut(
  planned_calendar_time = 45,
  target_event_overall = 350
)

# Example 1: regular logrank test at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = fh(rho = 0, gamma = 0)
)

# Example 2: weighted logrank test by FH(0, 0.5) at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = fh(rho = 0, gamma = 0.5)
)

# Example 3: weighted logrank test by MB(3) at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = mb(delay = 3)
)

# Example 4: weighted logrank test by early zero (6) at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = early_zero(6)
)

# Example 5: RMST at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = rmst,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  tau = 20
)

# Example 6: Milestone at all 3 analyses
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = milestone,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  ms_time = 10
)

# Warning: this example will be executable when we add info info0 to the milestone test
# Example 7: WLR with fh(0, 0.5) test at IA1,
# WLR with mb(6, Inf) at IA2, and milestone test at FA
ia1_test <- create_test(wlr, weight = fh(rho = 0, gamma = 0.5))
ia2_test <- create_test(wlr, weight = mb(delay = 6, w_max = Inf))
fa_test <- create_test(milestone, ms_time = 10)
## Not run: 
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = list(ia1 = ia1_test, ia2 = ia2_test, fa = fa_test),
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut)
)

## End(Not run)

# WARNING: Multiple tests per cut will be enabled in a future version.
#          Currently does not work.
# Example 8: At IA1, we conduct 3 tests, LR, WLR with fh(0, 0.5), and RMST test.
# At IA2, we conduct 2 tests, LR and WLR with early zero (6).
# At FA, we conduct 2 tests, LR and milestone test.
ia1_test <- list(
  test1 = create_test(wlr, weight = fh(rho = 0, gamma = 0)),
  test2 = create_test(wlr, weight = fh(rho = 0, gamma = 0.5)),
  test3 = create_test(rmst, tau = 20)
)
ia2_test <- list(
  test1 = create_test(wlr, weight = fh(rho = 0, gamma = 0)),
  test2 = create_test(wlr, weight = early_zero(6))
)
fa_test <- list(
  test1 = create_test(wlr, weight = fh(rho = 0, gamma = 0)),
  test3 = create_test(milestone, ms_time = 20)
)
## Not run: 
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = list(ia1 = ia1_test, ia2 = ia2_test, fa = fa_test),
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut)
)

## End(Not run)

# Example 9: regular logrank test at all 3 analyses in parallel
plan("multisession", workers = 2)
sim_gs_n(
  n_sim = 3,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = fh(rho = 0, gamma = 0)
)
plan("sequential")

Merck/simtrial documentation built on April 14, 2025, 5:37 a.m.