The R Journal: article published in 2020, volume 12:1

SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting PDF download
Emily Morris, Kevin He, Yanming Li, Yi Li and Jian Kang , The R Journal (2020) 12:1, pages 105-117.

Abstract 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.

Received: ; online 2020-09-10
CRAN packages: mboost, survival, Rcpp, RcppArmadillo, RcppParallel
CRAN Task Views implied by cited CRAN packages: HighPerformanceComputing, NumericalMathematics, Survival, ClinicalTrials, Econometrics, MachineLearning, SocialSciences


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@article{RJ-2020-018,
  author = {Emily Morris and Kevin He and Yanming Li and Yi Li and Jian
          Kang},
  title = {{SurvBoost: An R Package for High-Dimensional Variable
          Selection in the Stratified Proportional Hazards Model via
          Gradient Boosting}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-018},
  url = {https://doi.org/10.32614/RJ-2020-018},
  pages = {105--117},
  volume = {12},
  number = {1}
}