bssmle_se: Bootstrap varince-covariance estimation

In intccr: Semiparametric Competing Risks Regression under Interval Censoring

Bootstrap varince-covariance estimation

Description

Bootstrap varince estimation for the estimated regression coefficients

Usage

bssmle_se(formula, data, alpha, k = 1, do.par, nboot, objfun)

Arguments

formula	a formula object relating survival object Surv2(v, u, event) to a set of covariates
data	a data frame that includes the variables named in the formula argument
alpha	α = (α1, α2) contains parameters that define the link functions from class of generalized odds-rate transformation models. The components α1 and α2 should both be ≥ 0. If α1 = 0, the user assumes the proportional subdistribution hazards model or the Fine-Gray model for the cause of failure 1. If α2 = 1, the user assumes the proportional odds model for the cause of failure 2.
k	a parameter that controls the number of knots in the B-spline with 0.5 ≤ k ≤ 1
do.par	using parallel computing for bootstrap calculation. If do.par = TRUE, parallel computing will be used during the bootstrap estimation of the variance-covariance matrix for the regression parameter estimates.
nboot	a number of bootstrap samples for estimating variances and covariances of the estimated regression coefficients. If nboot = 0, the function ciregic does dot perform bootstrap estimation of the variance matrix of the regression parameter estimates and returns NA in the place of the estimated variance matrix of the regression parameter estimates.
objfun	an option to select estimating function

Details

The function bssmle_se estimates bootstrap standard errors for the estimated regression coefficients from the function bssmle, bssmle_lt, ro bssmle_ltir.

Value

The function bssmle_se returns a list of components:

notconverged	a list of number of bootstrap samples that did not converge
numboot	a number of bootstrap converged
Sigma	an estimated bootstrap variance-covariance matrix of the estimated regression coefficients

Author(s)

Giorgos Bakoyannis, [e-mail]

Jun Park, [e-mail]

intccr documentation built on May 10, 2022, 9:05 a.m.