🤖 AI Summary
This work addresses the challenge of survival analysis with right-censored data by proposing a training-free, end-to-end approach that introduces tabular foundation models (TFMs) into survival analysis for the first time. The method integrates the accelerated failure time (AFT) model with the Buckley–James estimator to perform nonparametric, iterative imputation of censored times within context. Requiring only the estimation of a single scalar parameter and no task-specific training, it effectively handles right-censoring while maintaining computational simplicity. On standard benchmarks, the proposed approach matches or even surpasses the performance of trained Cox regression and parametric AFT models, substantially enhancing the practicality and competitiveness of zero-shot survival regression.
📝 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.