  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 of the confounding variable, without
directly modeling the confounding effects. However, there is 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 and other confounders. Extensive
simulation studies demonstrate that in many scenarios SurvBoost can achieve a
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
the gene expression data with survival outcome in The Cancer Genome Atlas
(TCGA) study. In addition, a detailed hands-on tutorial for SurvBoost is
provided.
