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"),
...
)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.
Value
An object of class "twostageREC" containing:
- coef
Estimated coefficients.
- var
Variance-covariance matrix.
- theta
Dependence parameters.
- model
Model type.