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High-dimensional variable selection in the proportional hazards (PH) modelhas many successful applications in different areas. In practice, data mayinvolve confounding variables that do not satisfy the PH assumption, in whichcase the stratified proportional hazards (SPH) model can be adopted to controlthe confounding effects by stratification of the confounding variable, withoutdirectly modeling the confounding effects. However, there is lack ofcomputationally efficient statistical software for high-dimensional variableselection in the SPH model. In this work, an R package, SurvBoost, is developedto implement the gradient boosting algorithm for fitting the SPH model withhigh-dimensional covariate variables and other confounders. Extensivesimulation studies demonstrate that in many scenarios SurvBoost can achieve abetter selection accuracy and reduce computational time substantially comparedto the existing R package that implements boosting algorithms withoutstratification. The proposed R package is also illustrated by an analysis ofthe gene expression data with survival outcome in The Cancer Genome Atlas(TCGA) study. In addition, a detailed hands-on tutorial for SurvBoost isprovided.

SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting