Summary of A Large-scale Neutral Comparison Study Of Survival Models on Low-dimensional Data, by Lukas Burk et al.
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
by Lukas Burk, John Zobolas, Bernd Bischl, Andreas Bender, Marvin N. Wright, Raphael Sonabend
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the first large-scale benchmark experiment focused on single-event, right-censored, low-dimensional survival data. It aims to fill the gap in existing benchmarks by neutrally evaluating a broad range of methods and providing generalizable conclusions. The study benchmarks 18 models, ranging from classical statistical approaches to machine learning methods, on 32 publicly available datasets. The benchmark evaluates performance using 8 survival metrics, including discrimination measures, proper scoring rules, and overall predictive performance. The results show that no method significantly outperforms the Cox model, but Accelerated Failure Time models achieve better results for overall predictive performance. Machine learning methods like Oblique Random Survival Forests and Cox-based likelihood-boosting perform comparably well. The study concludes that the Cox Proportional Hazards model remains a simple and robust method for practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a big experiment to test many different ways to analyze survival data, which is important because it helps us understand how long things will last or when bad things might happen. They tested 18 different methods on 32 datasets and looked at how well each one did. They found that most methods didn’t do much better than the old Cox method, but some machine learning methods were pretty good too. The study is important because it helps us understand what methods work best for survival data. |
Keywords
» Artificial intelligence » Boosting » Likelihood » Machine learning
