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

TL;DR

SurvBoost is an R package that efficiently performs high-dimensional variable selection in stratified proportional hazards models using gradient boosting, improving accuracy and computational speed over existing methods.

Contribution

The paper introduces SurvBoost, a new R package that enables high-dimensional variable selection in stratified PH models with enhanced efficiency and accuracy.

Findings

SurvBoost outperforms existing packages in selection accuracy.

It significantly reduces computational time.

Demonstrated effectiveness on TCGA gene expression data.

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 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.