A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations

2026-01-09

AI Summary

This study addresses the challenge of efficiently and robustly estimating the difference in restricted mean survival time (RMST) under right-censored time-to-event data commonly encountered in clinical trials. The authors propose a novel framework that integrates targeted minimum loss-based estimation (TMLE) with pseudo-observations, marking the first application of this combination for RMST difference estimation. Additionally, they introduce an innovative replication reference approach to facilitate sensitivity analysis under right censoring. Empirical evaluation on real clinical trial data demonstrates that the proposed method substantially improves estimation efficiency while maintaining robustness, offering a theoretically sound and practically valuable tool for RMST-based comparative analyses in survival settings.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application.

Problem

• restricted mean survival time
• time-to-event outcomes
• clinical trial
• RMST difference
• right-censoring

Innovation

• targeted learning
• restricted mean survival time
• pseudo-observations
• TMLE
• sensitivity analysis