bssmle_aipw: B-spline Sieve Maximum Likelihood Estimation for...

In intccr: Semiparametric Competing Risks Regression under Interval Censoring

B-spline Sieve Maximum Likelihood Estimation for Interval-Censored Competing Risks Data and Missing Cause of Failure

Description

Routine that performs B-spline sieve maximum likelihood estimation with linear and nonlinear inequality and equality constraints

Usage

bssmle_aipw(formula, aux, data, alpha, k)

Arguments

formula	a formula object relating survival object Surv2(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	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 event type 1. If α2 = 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 ≤ k ≤ 1

Details

The function bssmle_aipw performs B-spline sieve maximum likelihood estimation.

Value

The function bssmle_aipw returns a list of components:

beta	a vector of the estimated coefficients for the B-splines
varnames	a vector containing variable names
varnames.aux	a vector containing auxiliary variable names
alpha	a vector of the link function parameters
loglikelihood	a loglikelihood of the fitted model
convergence	an indicator of convegence
tms	a vector of the minimum and maximum observation times
Bv	a list containing the B-splines basis functions evaluated at v

Author(s)

Jun Park, [e-mail]

Giorgos Bakoyannis, [e-mail]

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