Linear and nonlinear variable selection in competing risks data

Xiaowei Ren, Shanshan Li, Changyu Shen, Zhangsheng Yu

Research output: Contribution to journalArticle

1 Scopus citations


Subdistribution hazard model for competing risks data has been applied extensively in clinical researches. Variable selection methods of linear effects for competing risks data have been studied in the past decade. There is no existing work on selection of potential nonlinear effects for subdistribution hazard model. We propose a two-stage procedure to select the linear and nonlinear covariate(s) simultaneously and estimate the selected covariate effect(s). We use spectral decomposition approach to distinguish the linear and nonlinear parts of each covariate and adaptive LASSO to select each of the 2 components. Extensive numerical studies are conducted to demonstrate that the proposed procedure can achieve good selection accuracy in the first stage and small estimation biases in the second stage. The proposed method is applied to analyze a cardiovascular disease data set with competing death causes.

Original languageEnglish (US)
Pages (from-to)2134-2147
Number of pages14
JournalStatistics in Medicine
Issue number13
StatePublished - Jun 15 2018
Externally publishedYes


  • cubic b-spline
  • penalized log-likelihood
  • spectral decomposition
  • subdistribution hazard

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Linear and nonlinear variable selection in competing risks data'. Together they form a unique fingerprint.

Cite this