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 often narrow in scope, focusing, for
example, on high-dimensional data. Additionally, they may lack appropriate
tuning or evaluation procedures, or are 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
conclusions. We benchmark 18 models, ranging from classical statistical
approaches to many common machine learning methods, on 32 publicly available
datasets. The benchmark tunes for both a discrimination measure and a proper
scoring rule to assess performance in different settings. Evaluating on 8
survival metrics, we assess discrimination, calibration, and overall predictive
performance of the tested models. Using discrimination measures, we find that
no method significantly outperforms the Cox model. However, (tuned) Accelerated
Failure Time models were able to achieve significantly better results with
respect to overall predictive performance as measured by the right-censored
log-likelihood. Machine learning methods that performed comparably well include
Oblique Random Survival Forests under discrimination, and Cox-based
likelihood-boosting under overall predictive performance. We conclude that for
predictive purposes in the standard survival analysis setting of
low-dimensional, right-censored data, the Cox Proportional Hazards model
remains a simple and robust method, sufficient for practitioners.

在此论文中，我们进行了首个大规模的中性基准实验，重点关注单事件、右删失、低维度生存数据。我们对 18 种模型进行了评估，涵盖经典统计方法和常见的机器学习方法，并使用 32 个公开可用的数据集。通过性能评估，我们发现在低维度、右删失数据的标准生存分析环境中，Cox 比例风险模型仍然是足够简单和强大的方法。