Journal or Book Title
The R Journal
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.
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Lee, Chi Hyun; Zhou, Heng; Ning, Jing; Liu, Diane D.; and Shen, Yu, "CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model" (2020). Biostatistics and Epidemiology Faculty Publications Series.
12(1), 118-130 https://dx.doi.org/10.32614/rj-2020-024