The R Journal: article published in 2020, volume 12:1

CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R
Tao Sun and Ying Ding , The R Journal (2020) 12:1, pages 266-282.

Abstract Bivariate time-to-event data frequently arise in research areas such as clinical trials and epidemiological studies, where the occurrence of two events are correlated. In many cases, the exact event times are unknown due to censoring. The copula model is a popular approach for modeling correlated bivariate censored data, in which the two marginal distributions and the between margin dependence are modeled separately. This article presents the R package CopulaCenR, which is designed for modeling and testing bivariate data under right or (general) interval censoring in a regression setting. It provides a variety of Archimedean copula functions including a flexible two-parameter copula and different types of regression models (parametric and semiparametric) for marginal distributions. In particular, it implements a semiparametric transformation model for the margins with proportional hazards and proportional odds models being its special cases. The numerical optimization is based on a novel two-step algorithm. For the regression parameters, three likelihood-based tests (Wald, generalized score and likelihood ratio tests) are also provided. We use two real data examples to illustrate the key functions in CopulaCenR.

Received: 2019-08-20; online 2020-09-10
CRAN packages: CopulaCenR, survival, parfm, frailtypack, coxme, phmm, copula, VineCopula, CopulaRegression, gcmr, gamCopula, Copula.surv, Sunclarco, GJRM
CRAN Task Views implied by cited CRAN packages: Survival, Distributions, ClinicalTrials, Econometrics, ExtremeValue, Finance, Multivariate, SocialSciences

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2020-025,
  author = {Tao Sun and Ying Ding},
  title = {{CopulaCenR: Copula based Regression Models for Bivariate
          Censored Data in R}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-025},
  url = {https://doi.org/10.32614/RJ-2020-025},
  pages = {266--282},
  volume = {12},
  number = {1}
}