Analysis of Survival Data with Group Lasso

作者：Kim Jinseog; Sohn Insuk; Jung Sin Ho; Kim Sujong; Park Changyi^*

来源：Communications in Statistics - Simulation and Computation, 2012, 41(9): 1593-1605.

DOI：10.1080/03610918.2011.611311

• Discrete covariate
• Gene expression
• Survival analysis

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.

• 出版日期2012-10-1