sim_GLcox: Simulation of Two-Stage Recurrent Events Data

Description

Simulates data based on Cox/Cox or Cox/Ghosh-Lin structures for recurrent events with a terminal event.

Usage

sim_GLcox(
  n,
  base1,
  drcumhaz,
  var.z = 0,
  r1 = NULL,
  rd = NULL,
  rc = NULL,
  fz = NULL,
  fdz = NULL,
  model = c("twostage", "frailty", "shared"),
  type = NULL,
  share = 1,
  cens = NULL,
  nmin = 100,
  nmax = 1000
)

Value

A data frame with simulated recurrent events and terminal events, including frailty terms.

Arguments

n: Number of IDs.
base1: Baseline cumulative hazard for recurrent events.
drcumhaz: Baseline cumulative hazard for the terminal event.
var.z: Variance of the gamma frailty.
r1: Relative risk term for the recurrent event baseline.
rd: Relative risk term for the terminal event.
rc: Relative risk term for censoring.
fz: Function for transformation of the frailty term.
fdz: Function for transformation of the frailty term for death.
model: Model type: "twostage", "frailty", or "shared".
type: Type of simulation (default depends on model).
share: Proportion of shared random effects (for partially shared models).
cens: Censoring rate for exponential censoring.
nmin: Minimum number of points in the time grid (default 100).
nmax: Maximum number of points in the time grid (default 1000).

Author

Thomas Scheike

Details

type=3: Generates data from a Cox/Cox two-stage model.
type=2: Generates data from a Ghosh-Lin/Cox model.

If var.z=0, data is generated without dependence or frailty. If model="twostage", the default is to generate data from a Ghosh-Lin/Cox model. If type=3, data is generated with marginal Cox models (Cox/Cox).

Simulation is based on a linear approximation of the hazard for two-stage models on a time grid. The grid must be sufficiently fine.

References

Scheike (2025), Two-stage recurrent events random effects models, LIDA, to appear.