We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem focused on the competing risk analysis. RAEN is composed of three stages. (1) *split variables*. We randomly split the high dimensional data set into a number of lower dimensional, low correlation subsets with a de-correlation splitting algorithm. (2) *prescreen and estimate variable importance*. For each subgroup of variables, a penalized model is fit by minimizing the least square approximation of an elastic net objective function to many bootstrap samples. We screen relevant variables from each of the subgroups based on the bootstrap aggregation. A measure of importance is yielded from this step for each selected variable. (3) *merge, select and estimate variables*. We merge the screened variables from step 2 to one data set and perform another bootstrap aggregated penalized model fitting. Although we focus on competing risks analysis, the approach proposed can be applied to most of common regression models, including generalized linear models, Cox regression, quantile regression, and many others as special cases. Our algorithm naturally has a parallel structure, thus it can be easily implemented in a parallel architecture and applied to high or ultra-high dimensional problems with tens of thousands or more of predictors.