A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations
Methodology
2026-01-21 v2 Machine Learning
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.
Cite
@article{arxiv.2601.06296,
title = {A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations},
author = {Man Jin and Yixin Fang},
journal= {arXiv preprint arXiv:2601.06296},
year = {2026}
}