TraCeR: Transformer-Based Competing Risk Analysis with Longitudinal Covariates

• URL: http://arxiv.org/abs/2512.18129v1
• Date: Fri, 19 Dec 2025 23:24:47 GMT
• Title: TraCeR: Transformer-Based Competing Risk Analysis with Longitudinal Covariates
• Authors: Maxmillan Ries, Sohan Seth,
• Abstract summary: TraCeR is a transformer-based survival analysis framework.<n>It estimates the hazard function from a sequence of measurements.<n>Experiments on multiple real-world datasets demonstrate substantial and statistically significant performance improvements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior work largely focusing on cross-sectional features, and in assessing calibration of these models, with research primarily focusing on discrimination during evaluation. We introduce TraCeR, a transformer-based survival analysis framework for incorporating longitudinal covariates. Based on a factorized self-attention architecture, TraCeR estimates the hazard function from a sequence of measurements, naturally capturing temporal covariate interactions without assumptions about the underlying data-generating process. The framework is inherently designed to handle censored data and competing events. Experiments on multiple real-world datasets demonstrate that TraCeR achieves substantial and statistically significant performance improvements over state-of-the-art methods. Furthermore, our evaluation extends beyond discrimination metrics and assesses model calibration, addressing a key oversight in literature.

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