Copula-Based Deep Survival Models for Dependent Censoring

• URL: http://arxiv.org/abs/2306.11912v1
• Date: Tue, 20 Jun 2023 21:51:13 GMT
• Title: Copula-Based Deep Survival Models for Dependent Censoring
• Authors: Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan
• Abstract summary: This paper presents a parametric model of survival that extends modern non-linear survival analysis by relaxing the assumption of conditional independence. On synthetic and semi-synthetic data, our approach significantly improves estimates of survival distributions compared to the standard that assumes conditional independence in the data.
• Score: 10.962520289040336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow-up - which is a lower bound on the time until the event). We consider the challenge of survival prediction: learning, from such data, a predictive model that can produce an individual survival distribution for a novel instance. Many contemporary methods of survival prediction implicitly assume that the event and censoring distributions are independent conditional on the instance's covariates - a strong assumption that is difficult to verify (as we observe only one outcome for each instance) and which can induce significant bias when it does not hold. This paper presents a parametric model of survival that extends modern non-linear survival analysis by relaxing the assumption of conditional independence. On synthetic and semi-synthetic data, our approach significantly improves estimates of survival distributions compared to the standard that assumes conditional independence in the data.

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