Routine that performs B-spline sieve maximum likelihood estimation with linear and nonlinear inequality/equality constraints
bssmle_lt(formula, data, alpha, k = 1)a formula object relating survival object Surv2(w, v, u, event) to a set of covariates
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_lt returns a list of components:
a vector of the estimated coefficients
a vector containing 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 design matrix
a vector of w
a vector of v
a vector of u
a list containing the B-splines basis functions evaluated at w
a list containing the B-splines basis functions evaluated at v
a list containing the B-splines basis functions evaluated at u
a list containing the first derivative of the B-splines basis functions evaluated at w
a list containing the first derivative of the B-splines basis functions evaluated at v
a list containing the first derivative of the B-splines basis functions evaluated at u
a matrix of event indicator functions
The function bssmle_lt performs B-spline sieve maximum likelihood estimation for left-truncated and interval-censored competing risks data.