Abstract
We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 757-773 |
| Number of pages | 17 |
| Journal | Biostatistics |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 1 2014 |
Keywords
- AIDS
- Brier score
- C-index
- Competing risks
- Cumulative incidence function
- Ensemble
ASJC Scopus subject areas
- General Medicine
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