ParamCop: Estimation of a parametric dependent censoring model without...

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Estimation of a parametric dependent censoring model without covariates.

Description

Note that it is not assumed that the association parameter of the copula function is known, unlike most other papers in the literature. The details for implementing the methodology can be found in Czado and Van Keilegom (2023).

Usage

ParamCop(Y, Delta, Copula, Dist.T, Dist.C, start = c(1, 1, 1, 1))

Arguments

Y	Follow-up time.
Delta	Censoring indicator.
Copula	The copula family. This argument can take values from c("frank","gumbel","clayton","gaussian","indep").
Dist.T	The distribution to be used for the survival time T. This argument can take one of the values from c("lnorm", "weibull", "llogis").
Dist.C	The distribution to be used for the censoring time C. This argument can take one of the values from c("lnorm", "weibull", "llogis").
start	Starting values

Value

A table containing the minimized negative log-likelihood using the independence copula model, the estimated parameter values for the model with the independence copula, the minimized negative log-likelihood using the specified copula model and the estimated parameter values for the model with the specified copula.

References

Czado and Van Keilegom (2023). Dependent censoring based on parametric copulas. Biometrika, 110(3), 721-738.

Examples

tau = 0.75
Copula = "frank"
Dist.T = "weibull"
Dist.C = "lnorm"
par.T = c(2,1)
par.C = c(1,2)
n=1000

simdata<-TCsim(tau,Copula,Dist.T,Dist.C,par.T,par.C,n)

Y = simdata[[1]]
Delta = simdata[[2]]

ParamCop(Y,Delta,Copula,Dist.T,Dist.C)

depCensoring documentation built on April 4, 2025, 1:52 a.m.