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arXiv Machine Learning By Julie Alberge, Ga\"el Varoquaux, Judith Ab\'ecassis

The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks

Read the original on arXiv Machine Learning →

The paper investigates whether the choice of loss function or network architecture is more critical for survival analysis with competing risks. By independently varying three loss functions (two strictly proper) and four backbone architectures across 20 tabular and 3 pathology‑image datasets, the authors find that modelling cumulative incidence directly with censoring weighting consistently outperforms other losses, while the backbone must match the data modality. Proper scoring rules reveal that the loss dominates performance on competing‑risk data, whereas the architecture matters more on single‑event data, and the two factors compound rather than substitute.

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