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

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2020-06-01 DOI:10.32614/rj-2020-024
Chi Hyun Lee, Heng Zhou, Jing Ning, Diane D Liu, Yu Shen
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引用次数: 0

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.

CoxPhLb:一个在Cox模型下分析长度偏差数据的R包。
在包括流行的队列研究在内的各种应用中,经常遇到长度偏倚抽样的数据,并且在平稳性假设下被认为是左截尾数据的特殊情况。对于长度偏倚的数据,许多半参数回归方法已被提出,以模拟协变量与感兴趣的生存结果之间的关联。在本文中,我们简要回顾了在Cox模型(最常用的半参数模型)下为分析长度偏差数据而建立的统计方法,并介绍了实现这些方法的R包CoxPhLb。具体来说,该软件包包括诸如拟合Cox模型以探索协变量对生存时间的影响以及检查比例风险模型假设和平稳性假设等功能。我们用一个模拟数据示例和一个公开的真实数据集(钱宁之家数据)来说明该包的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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