CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R

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.

Tao Sun , Ying Ding
2020-09-10

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-025.zip

CRAN packages used

CopulaCenR, survival, parfm, frailtypack, coxme, phmm, copula, VineCopula, CopulaRegression, gcmr, gamCopula, Copula.surv, Sunclarco, GJRM

CRAN Task Views implied by cited packages

Survival, Distributions, ClinicalTrials, Econometrics, ExtremeValue, Finance, Multivariate, SocialSciences

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Sun & Ding, "CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R", The R Journal, 2020

BibTeX citation

@article{RJ-2020-025,
  author = {Sun, Tao and Ding, Ying},
  title = {CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R},
  journal = {The R Journal},
  year = {2020},
  note = {https://doi.org/10.32614/RJ-2020-025},
  doi = {10.32614/RJ-2020-025},
  volume = {12},
  issue = {1},
  issn = {2073-4859},
  pages = {266-282}
}