bssmle_se_aipw function

Bootstrap varince-covariance estimation for interval-censored competing risks data and missing cause of failure

Bootstrap varince estimation for the estimated regression coefficients

bssmle_se_aipw(formula, aux, data, alpha, k, do.par, nboot, w.cores = NULL)

Arguments

• formula: a formula object relating survival object mSurv(v, u, event) to a set of covariates
• aux: auxiliary variables that may be associated with the missingness and the outcome of interest
• 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 event type 1. If $\alpha2 = 1$, the user assumes the proportional odds model for the event type 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.
• w.cores: a number of cores that are assigned (the default is NULL)

Returns

The function bssmle_aipw_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_aipw_se estimates bootstrap standard errors for the estimated regression coefficients from the function bssmle.

Author(s)

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

Giorgos Bakoyannis, gbakogia@iu.edu