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arXiv · 2210.11366

Deep conditional transformation models for survival analysis

Gabriele Campanella·Lucas Kook·Ida Häggström·Torsten Hothorn·Thomas J. Fuchs
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Abstract

An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classifiers. Parametric, distribution-free approaches which make full use of survival time and censoring status have not received much attention. We present deep conditional transformation models (DCTMs) for survival outcomes as a unifying approach to parametric and semiparametric survival analysis. DCTMs allow the specification of non-linear and non-proportional hazards for both tabular and non-tabular data and extend to all types of censoring and truncation. On real and semi-synthetic data, we show that DCTMs compete with state-of-the-art DL approaches to survival analysis.

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Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs. 2022-10-20. Deep conditional transformation models for survival analysis. https://arxiv.org/abs/2210.11366

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