Debiased Machine Learning for Partially Linear Accelerated Failure Time Models

2026-08-07

AI Summary

This study addresses the challenge of robust inference for a target exposure variable in partially linear accelerated failure time (AFT) models with right-censored data. The authors propose, for the first time, a rank-based debiased machine learning framework that integrates orthogonalized rank-based U-statistics, censoring-corrected influence functions, and a block-pair cross-fitting strategy. This approach overcomes two critical limitations: the lack of Neyman orthogonality in conventional U-statistics and the inapplicability of standard cross-fitting under censoring. The method accommodates flexible covariate adjustment and enables valid inference even in finite samples. Extensive simulations and an application to electronic health records from the All of Us Research Program demonstrate its strong statistical performance and practical utility.

Abstract

The Cox model remains the default for survival analysis, but the proportional hazards assumption is often violated and hazard ratios can be difficult to interpret. Accelerated failure time (AFT) models provide an intuitive time-scale alternative, yet flexible covariate adjustment while preserving valid inference on a target exposure remains challenging. For the partially linear AFT model under right censoring, a rank-based debiased machine learning (DML) framework remains undeveloped: the rank-based pairwise moment is not Neyman orthogonal and standard cross-fitting does not directly apply to U-statistics. We develop the first such framework by combining an orthogonalized rank-based U-statistic, a censoring-corrected influence function, and block-pairwise cross-fitting, yielding valid inference under flexible nuisance estimation. Simulations and an application to All of Us electronic health record data demonstrate finite-sample performance and practical utility.

Problem

• Accelerated Failure Time model
• Debiased Machine Learning
• Partially Linear Model
• Right Censoring
• U-statistics

Innovation

• debiased machine learning
• accelerated failure time model
• Neyman orthogonality
• U-statistics
• cross-fitting