SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.
SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.
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DOI：
10.32614/rj-2020-018
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Kang J
中科院分区：
文献类型：
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作者：
Morris E;He K;Li Y;Li Y;Kang J
比例风险(PH)模型中的高维变量选择在不同领域有许多成功的应用。在实践中，数据可能包含不满足PH假设的混杂变量，在这种情况下，可以采用分层比例风险(SPH)模型来控制混杂效应，而不是直接对混杂效应进行建模。然而，对于SPH模型中高维变量的选择，目前还缺乏计算效率高的统计软件。在这项工作中，R包，SurvBoost，实现了梯度Boost算法，以拟合高维协变量SPH模型。仿真研究表明，在许多场景中，SurvBoost与现有的不分层实现Boosting算法的R包相比，可以获得更好的选择精度，并大大减少计算时间。癌症基因组图谱研究中对基因表达数据和生存结果的分析也说明了所建议的R包。此外，还提供了SurvBoost的详细实践教程。
High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies demonstrate that in many scenarios SurvBoost can achieve better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of gene expression data with survival outcome in The Cancer Genome Atlas study. In addition, a detailed hands-on tutorial for SurvBoost is provided.