GenData: Simulate Recurrent Events Data

In zrmacc/MCC: Functions for Comparing Mean Cumulative Count Curves

Simulate Recurrent Events Data

Description

Simulate recurrent events data using exponential censoring, gap, and death times. Status is coded as 0 for censoring, 1 for event, 2 for death.

• The subject-specific death rate is calculated as frailty x base_death_rate x exp(beta_death x covariates).

• The subject-specific event rate is calculated as frailty x base_event_rate x exp(beta_event x covariates).

Usage

GenData(
  base_death_rate = 0.25,
  base_event_rate = 1,
  beta_death = NULL,
  beta_event = NULL,
  censoring_rate = 0.25,
  covariates = NULL,
  frailty_variance = 0,
  min_death_rate = 0.05,
  min_event_rate = 0.05,
  n = 100,
  tau = 4
)

Arguments

base_death_rate	Baseline arrival rate for the terminal event.
base_event_rate	Baseline arrival rate for recurrent events.
beta_death	Numeric vector of log rate ratios for the death rate.
beta_event	Numeric vector of log rate ratios for the event rate.
censoring_rate	Arrival rate for the censoring time.
covariates	Numeric design matrix.
frailty_variance	Variance of the gamma frailty.
min_death_rate	Minimum subject-specific event rate. Must be positive.
min_event_rate	Minimum subject-specific event rate. Must be positive.
n	Number of subjects. Overwritten by 'nrow(covariates)' if covariates are provided.
tau	Truncation time.

Value

Data.frame, containing:

• The subject identifier 'idx' (index).

• 'time' and 'status' of the event: 0 for censoring, 1 for an event, 2 for death.

• The 'true_death_rate', 'true_event_rate', and 'frailty', which are subject-specific.

zrmacc/MCC documentation built on July 16, 2025, 4:04 p.m.