sim_comp_data: Simulate a complete clinical trial with event data drawn from a piecewise exponential distribution

Description

Simulate a complete clinical trial with event data drawn from a piecewise exponential distribution

Usage

sim_comp_data(
  hazard_treatment,
  hazard_control = NULL,
  cutpoints = 0,
  N_total,
  lambda = 0.3,
  lambda_time = 0,
  end_of_study,
  block = 2,
  rand_ratio = c(1, 1),
  prop_loss = 0
)

Value

A data frame with 1 row per subject and columns:

time:: numeric. Time of event or censoring time.
treatment:: integer. Treatment arm with values 1L for experimental arm, and 0L for control arm (only if hazard_control is given).
event:: integer. Indicator of whether event occurred (=1L if occurred and =0L if right-censored).
enrollment:: numeric. Time of patient enrollment relative to time trial enrolled first patient.
id:: integer. Identification number for each patient.
loss_to_fu:: logical. Indicator of whether the patient was lost to follow-up during the course of observation.

Arguments

hazard_treatment: vector. Constant hazard rates under the treatment arm.
hazard_control: vector. Constant hazard rates under the control arm.
cutpoints: vector. Times at which the baseline hazard changes. Default is cutpoints = 0, which corresponds to a simple (non-piecewise) exponential model.
N_total: integer. Maximum sample size allowable
lambda: vector. Enrollment rates across simulated enrollment times. See enrollment for more details.
lambda_time: vector. Enrollment time(s) at which the enrollment rates change. Must be same length as lambda. See enrollment for more details.
end_of_study: scalar. Length of the study; i.e. time at which endpoint will be evaluated.
block: scalar. Block size for generating the randomization schedule.
rand_ratio: vector. Randomization allocation for the ratio of control to treatment. Integer values mapping the size of the block. See randomization for more details.
prop_loss: scalar. Overall proportion of subjects lost to follow-up. Defaults to zero.