Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
This addresses a critical issue in survival analysis for applications like medical studies, where dependent censoring can bias results, and it is incremental by building on prior copula-based methods but with improved flexibility.
The paper tackles the problem of dependent censoring in survival analysis by proposing a deep learning method that eliminates the need to specify the ground truth copula, and experiments show it significantly reduces survival estimation bias compared to existing methods.
Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-tocensoring (such as loss of follow-up) is observed for each sample. The majority of existing machine learning-based survival analysis methods assume that survival is conditionally independent of censoring given a set of covariates; an assumption that cannot be verified since only marginal distributions is available from the data. The existence of dependent censoring, along with the inherent bias in current estimators has been demonstrated in a variety of applications, accentuating the need for a more nuanced approach. However, existing methods that adjust for dependent censoring require practitioners to specify the ground truth copula. This requirement poses a significant challenge for practical applications, as model misspecification can lead to substantial bias. In this work, we propose a flexible deep learning-based survival analysis method that simultaneously accommodate for dependent censoring and eliminates the requirement for specifying the ground truth copula. We theoretically prove the identifiability of our model under a broad family of copulas and survival distributions. Experiments results from a wide range of datasets demonstrate that our approach successfully discerns the underlying dependency structure and significantly reduces survival estimation bias when compared to existing methods.