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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-024.zip
CoxPhLb, survival, KMsurv, coxphw
Survival, ClinicalTrials, Econometrics, SocialSciences
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 ...".
For attribution, please cite this work as
Lee, et al., "The R Journal: CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model", The R Journal, 2020
BibTeX citation
@article{RJ-2020-024,
author = {Lee, Chi Hyun and Zhou, Heng and Ning, Jing and Liu, Diane D. and Shen, Yu},
title = {The R Journal: CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-024},
doi = {10.32614/RJ-2020-024},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {118-130}
}