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

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model
Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D. Liu and Yu Shen , The R Journal (2020) 12:1, pages 118-130.

Abstract Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for length biased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package CoxPhLb that implements these methods. Specifically, the package includes features such as fitting the Cox model to explore covariate effects on survival times and checking the proportional hazards model assumptions and the stationarity assumption. We illustrate usage of the package with a simulated data example and a real dataset, the Channing House data, which are publicly available.

Received: 2019-04-12; online 2020-09-10
CRAN packages: CoxPhLb, survival, KMsurv, coxphw
CRAN Task Views implied by cited CRAN packages: Survival, ClinicalTrials, Econometrics, SocialSciences

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

@article{RJ-2020-024,
  author = {Chi Hyun Lee and Heng Zhou and Jing Ning and Diane D. Liu
          and Yu Shen},
  title = {{CoxPhLb: An R Package for Analyzing Length Biased Data under
          Cox Model}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-024},
  url = {https://doi.org/10.32614/RJ-2020-024},
  pages = {118--130},
  volume = {12},
  number = {1}
}