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depCensoring (version 0.1.3)

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) and Willems et al. (2024+) .

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Install

install.packages('depCensoring')

Monthly Downloads

172

Version

0.1.3

License

GPL-3

Maintainer

Negera Wakgari Deresa

Last Published

October 17th, 2024

Functions in depCensoring (0.1.3)

Derivative of transform Cholesky decomposition to covariance matrix element.

Search function

dat.sim.reg.comp.risks

Data generation function for competing risks data

Derivative of transform Cholesky decomposition to covariance matrix.

likIFG.cmprsk.Cholesky

Full likelihood (including estimation of control function).

Transform Cholesky decomposition to covariance matrix parameter element.

Logarithmic transformation function.

Competing risk likelihood function.

Estimate a nonparametric transformation function

Estimate the control function

optimlikelihood

Fit the dependent censoring models.

loglike.indep.unconstrained

Log-likelihood function for the independence copula.

variance.cmprsk

Compute the variance of the estimates.

boot.nonparTrans

Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring

Transform Cholesky decomposition to covariance matrix

loglike.clayton.unconstrained

Log-likelihood function for the Clayton copula.

loglike.gumbel.unconstrained

Log-likelihood function for the Gumbel copula.

loglike.gaussian.unconstrained

Log-likelihood function for the Gaussian copula.

loglike.frank.unconstrained

Log-likelihood function for the Frank copula.

uniformize.data

Standardize data format

likF.cmprsk.Cholesky

Wrapper implementing likelihood function using Cholesky factorization.

power_transform

Power transformation function.

Second step log-likelihood function.

Second step log-likelihood function under independence assumption.

Distance between vectors

LikI.cmprsk.Cholesky

Wrapper implementing likelihood function assuming independence between competing risks and censoring using Cholesky factorization.

First step log-likelihood function for Z continuous

Fit a semiparametric transformation model for dependent censoring

Change H to long format

First step log-likelihood function for Z binary.

Compute bivariate survival probability

Second likelihood function needed to fit the independence model in the second step of the estimation procedure.

Derivative of the Yeo-Johnson transformation function

Estimate finite parameters based on score equations

Estimating equation for Ht1

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

estimate.cmprsk

Estimate the competing risks model of Rutten, Willems et al. (20XX).

Yeo-Johnson transformation function

Inverse Yeo-Johnson transformation function

Estimation of a parametric dependent censoring model without covariates.

Score equations of finite parameters