toydata

<p>This simulated datasets contains 1000 predictors, of which X1-X20, X41-X60 are true predictors.\  The first two columns are time to event competing risks and status.</p>

datasets

The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.

Han Sun

RAEN

Random Approximate Elastic Net (RAEN) Variable Selection Method

toydata function

<p>A dataframe of 200 rows and 1002 columns.</p>

toydata: Toy data for demonstration

Description

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