In this talk I will present a new sparse Cox regression method for high-dimensional massive sample size survival data. Our method is an L0-based iteratively reweighted L2-penalized Cox regression model, which inherits some appealing properties of both L0 and L2 penalized Cox regression while overcoming their limitations. We establish that it has an oracle property for selection and estimation and a grouping property for highly correlated covariates. We develop an efficient implementation for high-dimensional massive sample size survival data, which exhibits substantial speedups over its competitor in numerical studies. The performance of our method is illustrated using simulations and real data examples.