simtrial (version 1.0.0)

to_sim_pw_surv: Convert enrollment and failure rates from sim_fixed_n() to sim_pw_surv() format

Description

to_sim_pw_surv() converts failure rates and dropout rates entered in the simpler format for sim_fixed_n() to that used for sim_pw_surv(). The fail_rate argument for sim_fixed_n() requires enrollment rates, failure rates hazard ratios and dropout rates by stratum for a 2-arm trial, sim_pw_surv() is in a more flexible but less obvious but more flexible format. Since sim_fixed_n() automatically analyzes data and sim_pw_surv() just produces a simulation dataset, the latter provides additional options to analyze or otherwise evaluate individual simulations in ways that sim_fixed_n() does not.

Usage

to_sim_pw_surv(
  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))
)

Value

A list of two data frame components formatted for sim_pw_surv(): fail_rate and dropout_rate.

Arguments

fail_rate

Piecewise constant control group failure rates, hazard ratio for experimental vs. control, and dropout rates by stratum and time period.

Examples

# Example 1
# Convert standard input
to_sim_pw_surv()

# Stratified example
fail_rate <- data.frame(
  stratum = c(rep("Low", 3), rep("High", 3)),
  duration = rep(c(4, 10, 100), 2),
  fail_rate = c(
    .04, .1, .06,
    .08, .16, .12
  ),
  hr = c(
    1.5, .5, 2 / 3,
    2, 10 / 16, 10 / 12
  ),
  dropout_rate = .01
)

x <- to_sim_pw_surv(fail_rate)

# Do a single simulation with the above rates
# Enroll 300 patients in ~12 months at constant rate
sim <- sim_pw_surv(
  n = 300,
  stratum = data.frame(stratum = c("Low", "High"), p = c(.6, .4)),
  enroll_rate = data.frame(duration = 12, rate = 300 / 12),
  fail_rate = x$fail_rate,
  dropout_rate = x$dropout_rate
)

# Cut after 200 events and do a stratified logrank test
sim |>
  cut_data_by_event(200) |> # Cut data
  wlr(weight = fh(rho = 0, gamma = 0)) # Stratified logrank