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.
| Version: | 0.2 |
| Depends: | R (≥ 3.5.0), lars |
| Imports: | boot, foreach, doParallel, glmnet, fastcmprsk |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2021-02-21 |
| Author: | Han Sun and Xiaofeng Wang |
| Maintainer: | Han Sun <han.sunny at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/saintland/RAEN |
| NeedsCompilation: | no |
| CRAN checks: | RAEN results |
| Reference manual: | RAEN.pdf |
| Vignettes: |
RAEN_Tutorial |
| Package source: | RAEN_0.2.tar.gz |
| Windows binaries: | r-devel: RAEN_0.2.zip, r-release: RAEN_0.2.zip, r-oldrel: RAEN_0.2.zip |
| macOS binaries: | r-release: RAEN_0.2.tgz, r-oldrel: RAEN_0.1.tgz |
| Old sources: | RAEN archive |
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