A surrogate ℓ0 sparse Cox's regression with applications to sparse high-dimensional massive sample size time-to-event data

Eric S. Kawaguchi, Marc A. Suchard, Zhenqiu Liu, Gang Li

Research output: Contribution to journalArticle

Abstract

Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ0-based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of ℓ2-penalized regression method for sHDMSS time-to-event data. Specifically, we extend the ℓ0-based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ2-penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time-to-event data by leveraging existing efficient algorithms for massive ℓ2-penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time-to-event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates.

Original languageEnglish (US)
Pages (from-to)675-686
Number of pages12
JournalStatistics in Medicine
Volume39
Issue number6
DOIs
StatePublished - Mar 15 2020

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

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