boot.funI: Nonparametric bootstrap approach for the independent...

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Nonparametric bootstrap approach for the independent censoring model

Description

This function estimates the bootstrap standard errors for the finite-dimensional model parameters and for the non-parametric cumulative hazard function under the assumption of independent censoring. Parallel computing using foreach has been used to speed up the computation.

Usage

boot.funI(
  init,
  resData,
  X,
  W,
  lhat,
  cumL,
  dist,
  k,
  lb,
  ub,
  Obs.time,
  n.boot,
  n.iter,
  ncore,
  eps
)

Arguments

init	Initial values for the finite dimensional parameters obtained from the fit of fitIndepCens
resData	Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T and d2 = the censoring indicator of C.
X	Data matrix with covariates related to T
W	Data matrix with covariates related to C. First column of W should be a vector of ones
lhat	Initial values for the hazard function obtained from the fit of fitIndepCens based on the original data
cumL	Initial values for the cumulative hazard function obtained from the fit of fitIndepCens based on the original data
dist	The distribution to be used for the dependent censoring time C. Only two distributions are allowed, i.e, Weibull and lognormal distributions. With the value "Weibull" as the default
k	Dimension of X
lb	lower boundary for finite dimensional parameters
ub	Upper boundary for finite dimensional parameters
Obs.time	Observed survival time, which is the minimum of T, C and A, where A is the administrative censoring time.
n.boot	Number of bootstraps to use in the estimation of bootstrap standard errors.
n.iter	Number of iterations; the default is n.iter = 20. The larger the number of iterations, the longer the computational time
ncore	The number of cores to use for parallel computation is configurable, with the default ncore = 7.
eps	Convergence error. This is set by the user in such away that the desired convergence is met; the default is eps = 1e-3

Value

Bootstrap standard errors for parameter estimates and for estimated cumulative hazard function.

depCensoring documentation built on April 4, 2025, 1:52 a.m.