Lee Chi Hyun, Zhou Heng, Ning Jing, Liu Diane D, Shen Yu
Department of Biostatistics and Epidemiology, University of Massachusetts Amherst.
Biostatistics and Research Decision Sciences, Merck & Co., Inc.
R J. 2020 Jun;12(1):118-130. doi: 10.32614/rj-2020-024.
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 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.
受长度偏倚抽样影响的数据在包括现患队列研究在内的各种应用中经常遇到,并且在平稳性假设下被视为左截断数据的一种特殊情况。已经提出了许多半参数回归方法来处理长度偏倚数据,以对协变量与感兴趣的生存结果之间的关联进行建模。在本文中,我们简要回顾了在Cox模型(最常用的半参数模型)下为分析长度偏倚数据而建立的统计方法,并介绍了一个实现这些方法的R包。具体而言,该包包括诸如拟合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真实数据集(公开可用的钱宁之家数据)来说明该包的用法。