CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model

Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D. Liu, Yu Shen

Research output: Contribution to journalArticlepeer-review

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 lengthbiased 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.

Original languageEnglish (US)
Pages (from-to)118-130
Number of pages13
JournalR Journal
Volume12
Issue number1
DOIs
StatePublished - Jun 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

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