Analysis of survival data with group lasso

Jinseog Kim, Insuk Sohn, Sin Ho Jung, Sujong Kim, Changyi Park

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariates effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.

Original languageEnglish
Pages (from-to)1593-1605
Number of pages13
JournalCommunications in Statistics Part B: Simulation and Computation
Issue number9
StatePublished - 2012


  • Discrete covariate
  • Gene expression
  • Survival analysis


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