  Survival analysis is essential for studying time-to-event outcomes and
providing a dynamic understanding of the probability of an event occurring over
time. Various survival analysis techniques, from traditional statistical models
to state-of-the-art machine learning algorithms, support healthcare
intervention and policy decisions. However, there remains ongoing discussion
about their comparative performance. We conducted a comparative study of
several survival analysis methods, including Cox proportional hazards (CoxPH),
stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF),
Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv,
time-dependent Cox model based on neural network (CoxTime), and DeepHit
survival neural network. We applied the concordance index (C-index) for model
goodness-of-fit, and integral Brier scores (IBS) for calibration, and
considered the model interpretability. As a case study, we performed a
retrospective analysis of patients admitted through the emergency department of
a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality
based on patient demographics, clinicopathological features, and historical
data. The results of the C-index indicate that deep learning achieved
comparable performance, with DeepSurv producing the best discrimination
(DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv
(IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS:
0.0421), all using the full variables. Moreover, AutoScore-Survival, using a
minimal variable subset, is easy to interpret, and can achieve good
discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models
were satisfactory, DeepSurv exhibited the best discrimination and calibration.
In addition, AutoScore-Survival offers a more parsimonious model and excellent
interpretability.
