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depCensoring (version 0.1.8)

Statistical Methods for Survival Data with Dependent Censoring

Description

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) , Czado and Van Keilegom (2023) , Crommen et al. (2024) , Deresa and Van Keilegom (2024) , Willems et al. (2025) , Ding and Van Keilegom (2025) and D'Haen et al. (2025) .

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Version

Install

install.packages('depCensoring')

Monthly Downloads

348

Version

0.1.8

License

GPL-3

Maintainer

Negera Wakgari Deresa

Last Published

November 6th, 2025

Functions in depCensoring (0.1.8)

Likelihood.Profile.Kernel

Calculate the profiled likelihood function with kernel smoothing

Estimate a nonparametric transformation function

Estimation of a parametric dependent censoring model without covariates.

Search function

Estimate the model of D'Haen et al. (2025).

Estimate finite parameters based on score equations

Cumulative hazard function of survival time under dependent censoring

Cumulative hazard function of survival time under independent censoring

Score equations of finite parameters

Parameters.Constraints

Generate constraints of parameters

Estimating equation for Ht1

copdist.Archimedean

The distribution function of the Archimedean copula

Function to simulate (Y,Delta) from the copula based model for (T,C).

control.arguments

Prepare initial values within the control arguments

Calculate the goodness-of-fit test statistic

Estimated survival function based on Kaplan-Meier estimator

Maximum likelihood estimator for a given parametric distribution

Estimated survival function based on copula-graphic estimator (Archimedean copula only)

boot.nonparTrans

Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring

SurvMLE.Likelihood

Likelihood for a given parametric distribution

Semiparametric Estimation of the Survival Function under Dependent Censoring

estimate.cmprsk

Estimate the competing risks model of Willems et al. (2025).

Convert the Kendall's tau into the copula parameter

generator.Archimedean

The generator function of the Archimedean copula

Fit Independent Censoring Models

Convert the copula parameter the Kendall's tau

The h-function of the copula

Fit Dependent Censoring Models

loglike.clayton.unconstrained

Log-likelihood function for the Clayton copula.

loglike.frank.unconstrained

Log-likelihood function for the Frank copula.

loglike.indep.unconstrained

Log-likelihood function for the independence copula.

optimlikelihood

Fit the dependent censoring models.

Estimate the model of Willems et al. (2025).

summary.indepFit

Summary of indepCensoringFit object

Liver cirrhosis data set.

Summary of depCensoringFit object

Obtain the adjustment value of truncation

Obtain the value of the density function

Obtain the value of the distribution function

loglike.gaussian.unconstrained

Log-likelihood function for the Gaussian copula.

loglike.gumbel.unconstrained

Log-likelihood function for the Gumbel copula.

Likelihood.Semiparametric

Calculate the semiparametric version of profiled likelihood function

Compute bivariate survival probability

Chronometer object

Likelihood.Profile.Solve

Solve the profiled likelihood function

Likelihood.Parametric

Calculate the likelihood function for the fully parametric joint distribution

Change H to long format

Distance between vectors

Fit a semiparametric transformation model for dependent censoring

Calculate the kernel function