simtrial (version 1.0.0)

sim_pw_surv: 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 distribution 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,
  stratum = data.frame(stratum = "All", p = 1),
  block = c(rep("control", 2), rep("experimental", 2)),
  enroll_rate = data.frame(rate = 9, duration = 1),
  fail_rate = data.frame(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)),
  dropout_rate = data.frame(stratum = rep("All", 2), period = rep(1, 2), treatment =
    c("control", "experimental"), duration = rep(100, 2), rate = rep(0.001, 2))
)

Value

A data frame with the following variables for each observation:

• stratum: Stratum for the observation.

• 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().

• dropout_time: Dropout time generated using rpwexp().

• cte: Calendar time of enrollment plus 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.

The data frame also has the attribute "ratio", which is calculated as the number of "experimental" treatments divided by the number of "control" treatments from the input argument block.

Arguments

n

Number of observations. If length(n) > 1, the length is taken to be the number required.

stratum

A data frame with stratum specified in stratum, probability (incidence) of each stratum in p.

block

Vector of treatments to be included in each block. Also used to calculate the attribute "ratio" (for more details see the section Value below).

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.

dropout_rate

Dropout rates; see details and examples; note that treatments need to be the same as input in block.

Examples

library(dplyr)

# 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 stratum; will use defaults for blocking and enrollRates
sim_pw_surv(
  n = 20,
  # 2 stratum,30% and 70% prevalence
  stratum = data.frame(stratum = c("Low", "High"), p = c(.3, .7)),
  fail_rate = data.frame(
    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)
  ),
  dropout_rate = data.frame(
    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 data.frame
fail_rate <- bind_rows(
  data.frame(stratum = "Low", period = 1, treatment = "control", duration = 3, rate = .03),
  data.frame(stratum = "Low", period = 1, treatment = "experimental", duration = 3, rate = .03),
  data.frame(stratum = "Low", period = 2, treatment = "experimental", duration = 3, rate = .02),
  data.frame(stratum = "High", period = 1, treatment = "control", duration = 3, rate = .05),
  data.frame(stratum = "High", period = 1, treatment = "experimental", duration = 3, rate = .06),
  data.frame(stratum = "High", period = 2, treatment = "experimental", duration = 3, rate = .03)
)

dropout_rate <- bind_rows(
  data.frame(stratum = "Low", period = 1, treatment = "control", duration = 3, rate = .001),
  data.frame(stratum = "Low", period = 1, treatment = "experimental", duration = 3, rate = .001),
  data.frame(stratum = "High", period = 1, treatment = "control", duration = 3, rate = .001),
  data.frame(stratum = "High", period = 1, treatment = "experimental", duration = 3, rate = .001)
)

sim_pw_surv(
  n = 12,
  stratum = data.frame(stratum = c("Low", "High"), p = c(.3, .7)),
  fail_rate = fail_rate,
  dropout_rate = dropout_rate
)