Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models

• URL: http://arxiv.org/abs/2606.03689v1
• Date: Tue, 02 Jun 2026 14:11:23 GMT
• Title: Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models
• Abstract summary: Tabular Foundation Models (TFM) have attracted significant interest in recent years due to their ability to perform prediction tasks in a single forward pass.<n>We present a training-free method to survival regression by leveraging TFMs to both predict the time of the event and iteratively impute right-censored data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs. Widely used in several domains, including healthcare and churn prediction, a central challenge in its applicability stems from the time of the event being partially observed or \emph{right-censoring}. Tabular Foundation Models (TFM) have attracted significant interest in recent years due to their ability to perform prediction tasks in a single forward pass, requiring no dataset-specific parameter fitting. Despite their success, their application to prediction tasks on time-to-event data remains difficult due to right censoring. In this work, we present a training-free method to survival regression by leveraging TFMs to both predict the time of the event and iteratively impute right-censored data. Our method uses a TFM to construct an Accelerated Failure Time (AFT) model requiring no training beyond fitting a single scalar parameter. Subsequently, by building on the Buckley-James estimator, we introduce a non-parametric in-context estimator for right-censored data. Our experiments on standard survival analysis benchmarks show that our method is competitive with several parametric and semi-parametric survival regression models that require training, including Cox regression and parametric AFT models.

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