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Fittting of Two-stage recurrent events random effects model based on Cox/Cox or Cox/Ghosh-Lin marginals

Source: R/recreg.R
twostageREC.Rd

Fittting of Two-stage recurrent events random effects model based on Cox/Cox or Cox/Ghosh-Lin marginals. Random effects model fore recurrent events with terminal event. Marginal models fitted first and given to twostageREC 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 terminal event

recurrent

marginal model for recurrent events

data

used for fitting

theta

starting value for total variance of gamma frailty

model

can fully shared "full", partly shared "shared", or non-shared where the random effect acts only on recurrent events

ghosh.lin

to force use ghosh.lin marginals based on recurrent (taking baseline and coefficients)

theta.des

regression design for variance

possible link function 1 is exponential link

method

NR

no.opt

to not optimize

weights

possible weights

se.cluster

to combine influence functions for naive variance based on these clusters GEE style

fnu

a function to make transformation for nu (amount shared)

nufix

to fix the amount shared

nu

starting value for amount shared

numderiv

uses numerical derivatives for some derivatives

derivmethod

method for numerical derivative

...

arguments for

References

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