Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time. In this work, we show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior of a given population, and (2) independently distributed survival scores, which model idiosyncratic probabilistic dynamics of its given members, in a fully parametric setting. The proposed inference method is shown to dynamically handle right-censored observation horizons, and to achieve competitive performance when compared to other state-of-the-art methods in a variety of real-world datasets, including computationally inefficient Deep Learning-based SA methods and models that require MCMC for inference. Nevertheless, our method achieves robust results from the outset, while not being subjected to fine-tuning or hyperparameter optimization.

通过将Survival Analysis模型的完整表达分解为聚合基线风险和独立分布的生存得分，改善了该模型的训练和推断方法，实现了对右删失观测的动态处理，并在各种真实世界数据集中取得了与其他最先进方法相媲美的性能，无需进行微调或超参数优化。

复合生存分析：学习辅助聚合基线和生存得分