twostageREC: Fitting of Two-Stage Recurrent Events Random Effects Model

Description

Fits a two-stage random effects model for recurrent events with a terminal event. Marginal models (Cox or Ghosh-Lin) are fitted first and passed to this function.

Usage

twostageREC(
  margsurv,
  recurrent,
  data = parent.frame(),
  theta = NULL,
  model = c("full", "shared", "non-shared"),
  ghosh.lin = NULL,
  theta.des = NULL,
  var.link = 0,
  method = "NR",
  no.opt = FALSE,
  weights = NULL,
  se.cluster = NULL,
  fnu = NULL,
  nufix = 0,
  nu = NULL,
  numderiv = 1,
  derivmethod = c("simple", "Richardson"),
  ...
)

Value

An object of class "twostageREC" containing:

coef: Estimated coefficients.
var: Variance-covariance matrix.
theta: Dependence parameters.
model: Model type.

Arguments

margsurv: Marginal model for the terminal event (object of class "phreg").
recurrent: Marginal model for recurrent events (object of class "phreg" or "recreg").
data: Data frame used for fitting.
theta: Starting value for total variance of gamma frailty.
model: Model type: "full" (fully shared), "shared" (partly shared), or "non-shared".
ghosh.lin: Logical; if TRUE, forces use of Ghosh-Lin marginals based on the recurrent model.
theta.des: Regression design for variance parameters.
var.link: Link function for variance (1 for exponential).
method: Optimization method (default "NR").
no.opt: Logical; if TRUE, skips optimization.
weights: Weights.
se.cluster: Clusters for SE calculation (GEE style).
fnu: Function to transform \(\nu\) (amount shared).
nufix: Logical; if TRUE, fixes the amount shared.
nu: Starting value for the amount shared.
numderiv: Logical; if TRUE, uses numerical derivatives.
derivmethod: Method for numerical derivative.
...: Arguments for the optimizer.

Author

Thomas Scheike

Details

Supports:

Cox/Cox marginals.
Cox/Ghosh-Lin marginals.
Fully shared, partly shared, or non-shared random effects.

References

Scheike (2026), Two-stage recurrent events random effects models, LIDA, to appear.