Deep conditional transformation models for survival analysis
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.
Cite
@article{arxiv.2210.11366,
title = {Deep conditional transformation models for survival analysis},
author = {Gabriele Campanella and Lucas Kook and Ida Häggström and Torsten Hothorn and Thomas J. Fuchs},
journal= {arXiv preprint arXiv:2210.11366},
year = {2022}
}