sim_pw_surv: Simulate a stratified time-to-event outcome randomized trial

In keaven/simtrial: Clinical Trial Simulation

Simulate a stratified time-to-event outcome randomized trial

Description

sim_pw_surv enables simulation of a clinical trial with essentially arbitrary patterns of enrollment, failure rates and censoring. The piecewise exponential distribution allows a simple method to specify a distribtuion and enrollment pattern where the enrollment, failure and dropout rate changes over time. While the main purpose may be to generate a trial that can be analyzed at a single point in time or using group sequential methods, the routine can also be used to simulate an adaptive trial design. Enrollment, failure and dropout rates are specified by treatment group, stratum and time period. Fixed block randomization is used; blocks must include treatments provided in failure and dropout specification. Default arguments are set up to allow very simple implementation of a non-proportional hazards assumption for an unstratified design.

Usage

sim_pw_surv(
  n = 100,
  strata = tibble(Stratum = "All", p = 1),
  block = c(rep("Control", 2), rep("Experimental", 2)),
  enroll_rate = tibble(rate = 9, duration = 1),
  fail_rate = tibble(Stratum = rep("All", 4), period = rep(1:2, 2), Treatment =
    c(rep("Control", 2), rep("Experimental", 2)), duration = rep(c(3, 1), 2), rate =
    log(2)/c(9, 9, 9, 18)),
  dropoutRates = tibble(Stratum = rep("All", 2), period = rep(1, 2), Treatment =
    c("Control", "Experimental"), duration = rep(100, 2), rate = rep(0.001, 2))
)

Arguments

n	Number of observations. If length(n) > 1, the length is taken to be the number required.
strata	A tibble with strata specified in Stratum, probability (incidence) of each stratum in p
block	Vector of treatments to be included in each block
enroll_rate	Enrollment rates; see details and examples
fail_rate	Failure rates; see details and examples; note that treatments need to be the same as input in block
dropoutRates	Dropout rates; see details and examples; note that treatments need to be the same as input in block

Value

a tibble with the following variables for each observation Stratum, enroll_time (enrollment time for the observation), Treatment (treatment group; this will be one of the values in the input block), fail_time (failure time generated using rpwexp()), dropoutTime (dropout time generated using rpwexp()), cte (calendar time of enrollment plot the minimum of failure time and dropout time), fail (indicator that cte was set using failure time; i.e., 1 is a failure, 0 is a dropout).

Examples

library(dplyr)
library(tibble)

# example 1
sim_pw_surv(n = 20)

# example 2
# 3:1 randomization
sim_pw_surv(n = 20,
          block = c(rep("Experimental",3), "Control"))

# example 3
# Simulate 2 strata; will use defaults for blocking and enrollRates
sim_pw_surv(n = 20,
          # 2 strata,30% and 70% prevalence
          strata = tibble(Stratum = c("Low","High"), p = c(.3, .7)),
          fail_rate = tibble(Stratum = c(rep("Low", 4), rep("High", 4)),
                             period = rep(1:2, 4),
                             Treatment = rep(c(rep("Control", 2),
                                         rep("Experimental", 2)), 2),
                             duration = rep(c(3,1), 4),
                             rate = c(.03, .05, .03, .03, .05, .08, .07, .04)),
          dropoutRates = tibble(Stratum = c(rep("Low", 2), rep("High", 2)),
                                period = rep(1, 4),
                                Treatment = rep(c("Control", "Experimental"), 2),
                                duration = rep(1, 4),
                                rate = rep(.001, 4)))
# example 4
# If you want a more rectangular entry for a tibble
fail_rate <- bind_rows(
  tibble(Stratum = "Low" , period = 1, Treatment = "Control"     , duration = 3, rate = .03),
  tibble(Stratum = "Low" , period = 1, Treatment = "Experimental", duration = 3, rate = .03),
  tibble(Stratum = "Low" , period = 2, Treatment = "Experimental", duration = 3, rate = .02),
  tibble(Stratum = "High", period = 1, Treatment = "Control"     , duration = 3, rate = .05),
  tibble(Stratum = "High", period = 1, Treatment = "Experimental", duration = 3, rate = .06),
  tibble(Stratum = "High", period = 2, Treatment = "Experimental", duration = 3, rate = .03))

dropoutRates <- bind_rows(
  tibble(Stratum = "Low" , period=1, Treatment = "Control"     , duration = 3, rate = .001),
  tibble(Stratum = "Low" , period=1, Treatment = "Experimental", duration = 3, rate = .001),
  tibble(Stratum = "High", period=1, Treatment = "Control"     , duration = 3, rate = .001),
  tibble(Stratum = "High", period=1, Treatment = "Experimental", duration = 3, rate = .001))

sim_pw_surv(n = 12,
         strata = tibble(Stratum = c("Low","High"), p = c(.3, .7)),
         fail_rate = fail_rate,
         dropoutRates = dropoutRates)

keaven/simtrial documentation built on April 17, 2023, 4:03 a.m.