bssmle_se function

Bootstrap varince-covariance estimation

Bootstrap varince estimation for the estimated regression coefficients

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: $\alpha = (\alpha1, \alpha2)$ contains parameters that define the link functions from class of generalized odds-rate transformation models. The components $\alpha1$ and $\alpha2$ should both be $\ge 0$. If $\alpha1 = 0$, the user assumes the proportional subdistribution hazards model or the Fine-Gray model for the cause of failure 1. If $\alpha2 = 1$, 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 $0.5 \le$k$\le 1$
• 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

Returns

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

Details

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

Author(s)

Giorgos Bakoyannis, gbakogia@iu.edu

Jun Park, jun.park@alumni.iu.edu