Routine that performs B-spline sieve maximum likelihood estimation with linear and nonlinear inequality and equality constraints
bssmle_aipw(formula, aux, data, alpha, k)a formula object relating survival object Surv2(v, u, event) to a set of covariates
auxiliary variables that may be associated with the missingness and the outcome of interest
a data frame that includes the variables named in the formula argument
\(\alpha = (\alpha1, \alpha2)\) 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.
a parameter that controls the number of knots in the B-spline with \(0.5 \le \)k\( \le 1\)
The function bssmle_aipw returns a list of components:
a vector of the estimated coefficients for the B-splines
a vector containing variable names
a vector containing auxiliary variable names
a vector of the link function parameters
a loglikelihood of the fitted model
an indicator of convegence
a vector of the minimum and maximum observation times
a list containing the B-splines basis functions evaluated at v
The function bssmle_aipw performs B-spline sieve maximum likelihood estimation.