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Activity Number: 253 - Contributed Poster Presentations: Section on Statistical Computing
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #328624
Title: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting
Author(s): Emily Morris* and Jian Kang and Zhi He and Yanming Li and Yi Li
Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan and University of Michigan
Keywords: Variable selection; Survival analysis; Boosting

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. 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.

Authors who are presenting talks have a * after their name.

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