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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-018.zip
mboost, survival, Rcpp, RcppArmadillo, RcppParallel
HighPerformanceComputing, NumericalMathematics, Survival, ClinicalTrials, Econometrics, MachineLearning, SocialSciences
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Morris, et al., "The R Journal: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting", The R Journal, 2020
BibTeX citation
@article{RJ-2020-018,
author = {Morris, Emily and He, Kevin and Li, Yanming and Li, Yi and Kang, Jian},
title = {The R Journal: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-018},
doi = {10.32614/RJ-2020-018},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {105-117}
}