[2406.04098] A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data

Statistics > Machine Learning arXiv:2406.04098 (stat) [Submitted on 6 Jun 2024 (v1), last revised 28 Feb 2026 (this version, v2)] Title:A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data Authors:Lukas Burk, John Zobolas, Bernd Bischl, Andreas Bender, Marvin N. Wright, Raphael Sonabend View a PDF of the paper titled A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data, by Lukas Burk and 5 other authors View PDF HTML (experimental) Abstract:This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation. Existing benchmarks in the survival literature are smaller in scale regarding the number of used datasets and extent of empirical evaluation. They often lack appropriate tuning or evaluation procedures, while other comparison studies focus on qualitative reviews rather than quantitative comparisons. This comprehensive study aims to fill the gap by neutrally evaluating a broad range of methods and providing generalizable guidelines for practitioners. We benchmark 19 models, ranging from classical statistical approaches to many common machine learning methods, on 34 publicly available datasets. The benchmark tunes models using both a discrimination measure (Harrell's C-index) and a scorin...