SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
- URL: http://arxiv.org/abs/2302.12749v1
- Date: Fri, 24 Feb 2023 17:03:51 GMT
- Title: SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
- Authors: Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela
van der Schaar
- Abstract summary: Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis.
We propose SurvivalGAN, a generative model that handles survival data by addressing the imbalance in the censoring and event horizons.
We evaluate this method via extensive experiments on medical datasets.
- Score: 121.84429525403694
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Synthetic data is becoming an increasingly promising technology, and
successful applications can improve privacy, fairness, and data
democratization. While there are many methods for generating synthetic tabular
data, the task remains non-trivial and unexplored for specific scenarios. One
such scenario is survival data. Here, the key difficulty is censoring: for some
instances, we are not aware of the time of event, or if one even occurred.
Imbalances in censoring and time horizons cause generative models to experience
three new failure modes specific to survival analysis: (1) generating too few
at-risk members; (2) generating too many at-risk members; and (3) censoring too
early. We formalize these failure modes and provide three new generative
metrics to quantify them. Following this, we propose SurvivalGAN, a generative
model that handles survival data firstly by addressing the imbalance in the
censoring and event horizons, and secondly by using a dedicated mechanism for
approximating time-to-event/censoring. We evaluate this method via extensive
experiments on medical datasets. SurvivalGAN outperforms multiple baselines at
generating survival data, and in particular addresses the failure modes as
measured by the new metrics, in addition to improving downstream performance of
survival models trained on the synthetic data.
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