Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine
Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine
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DOI：
10.1093/pcmedi/pbz007
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发表时间：
2019-06-01
影响因子：
5.3
通讯作者：
Li, Yi
中科院分区：
文献类型：
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作者：
Hong, Hyokyoung G.;Christiani, David C.;Li, Yi
分位数回归将结果的整个分布与感兴趣的协变量联系起来，并已成为常用回归模型的重要替代方案。然而，删失数据的存在，如生存时间，往往是癌症研究的主要终点，阻碍了分位数回归技术的使用，因为数据的不完整性。随着精准医疗时代的到来和高通量数据的可用性，具有高维预测因子的分位数回归引起了人们的广泛关注，并与传统回归方法相比提供了更多的见解。本文提供了一个实用的指导使用分位数回归右删失结果数据的协变量的低或高维。我们使用波士顿肺癌幸存者队列的数据集进行讨论，这是一项基于医院的前瞻性队列研究，旨在扩大癌症研究的范围，最大限度地利用收集的数据，并提供有用的统计替代方案。我们使用分位数回归来识别与高危肺癌患者（例如生存期短的患者）相关的临床和分子预测因子，例如CpG甲基化位点。
Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models. However, the presence of censored data such as survival time, often the main endpoint in cancer studies, has hampered the use of quantile regression techniques because of the incompleteness of data. With the advent of the precision medicine era and availability of high throughput data, quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches. This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low- or high-dimensionality. We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort, a hospital-based prospective cohort study, with the goals of broadening the scope of cancer research, maximizing the utility of collected data, and offering useful statistical alternatives. We use quantile regression to identify clinical and molecular predictors, for example CpG methylation sites, associated with high-risk lung cancer patients, for example those with short survival.