simtrial (version 1.0.2)

sim_fixed_n: Simulation of fixed sample size design for time-to-event endpoint

Description

sim_fixed_n() provides simulations of a single endpoint two-arm trial where the enrollment, hazard ratio, and failure and dropout rates change over time.

Usage

sim_fixed_n(
  n_sim = 1000,
  sample_size = 500,
  target_event = 350,
  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)),
  total_duration = 30,
  block = rep(c("experimental", "control"), 2),
  timing_type = 1:5,
  rho_gamma = data.frame(rho = 0, gamma = 0)
)

Value

A data frame including columns:

• event: Event count.

• ln_hr: Log-hazard ratio.

• z: Normal test statistic; < 0 favors experimental.

• cut: Text describing cutoff used.

• duration: Duration of trial at cutoff for analysis.

• sim: Sequential simulation ID.

One row per simulated dataset per cutoff specified in timing_type, per test statistic specified. If multiple Fleming-Harrington tests are specified in rho_gamma, then columns rho and gamma are also included.

Arguments

n_sim

Number of simulations to perform.

sample_size

Total sample size per simulation.

target_event

Targeted event count for analysis.

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.

total_duration

Total follow-up from start of enrollment to data cutoff.

block

As in sim_pw_surv(). Vector of treatments to be included in each block.

timing_type

A numeric vector determining data cutoffs used; see details. Default is to include all available cutoff methods.

rho_gamma

A data frame with variables rho and gamma, both greater than equal to zero, to specify one Fleming-Harrington weighted logrank test per row.

Details

timing_type has up to 5 elements indicating different options for data cutoff:

• 1: Uses the planned study duration.

• 2: The time the targeted event count is achieved.

• 3: The planned minimum follow-up after enrollment is complete.

• 4: The maximum of planned study duration and targeted event count cuts (1 and 2).

• 5: The maximum of targeted event count and minimum follow-up cuts (2 and 3).

Examples

library(dplyr)
library(future)

# Example 1: logrank test ----
x <- sim_fixed_n(n_sim = 10, timing_type = 1, rho_gamma = data.frame(rho = 0, gamma = 0))
# Get power approximation
mean(x$z <= qnorm(.025))

# Example 2: WLR with FH(0,1) ----
sim_fixed_n(n_sim = 1, timing_type = 1, rho_gamma = data.frame(rho = 0, gamma = 1))
# Get power approximation
mean(x$z <= qnorm(.025))

# \donttest{
# Example 3: MaxCombo, i.e., WLR-FH(0,0)+ WLR-FH(0,1)
# Power by test
# Only use cuts for events, events + min follow-up
x <- sim_fixed_n(
  n_sim = 10,
  timing_type = 2,
  rho_gamma = data.frame(rho = 0, gamma = c(0, 1))
)

# Get power approximation
x |>
  group_by(sim) |>
  filter(row_number() == 1) |>
  ungroup() |>
  summarize(power = mean(p_value < .025))

# Example 4
# Use two cores
set.seed(2023)
plan("multisession", workers = 2)
sim_fixed_n(n_sim = 10)
plan("sequential")

# Example 5: Stratified design with two strata
sim_fixed_n(
  n_sim = 100,
  sample_size = 500,
  target_event = 350,
  stratum = data.frame(stratum = c("Biomarker-positive", "Biomarker-negative"),
                       p = c(0.5, 0.5)),
  enroll_rate = data.frame(stratum = c("Biomarker positive", "Biomarker negative"),
                           duration = c(12, 12), rate = c(250 / 12, 250 / 12)),
  fail_rate = data.frame(stratum = c(rep("Biomarker-positive", 2), rep("Biomarker-negative", 2)),
                         duration = c(3, Inf, 3, Inf),
                         fail_rate = log(2) / c(12, 12, 8, 8),
                         hr = c(1, 0.6, 1, 0.7),
                         dropout_rate = rep(0.001, 4)),
  total_duration = 30,
  block = rep(c("experimental", "control"), 2),
  timing_type = 1:5,
  rho_gamma = data.frame(rho = 0, gamma = 0)
  )
# }