Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
Weijia Zhang,
Chun Kai Ling,
Xuanhui Zhang
Abstract:Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-to censoring (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… Show more
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