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

TL;DR

This paper introduces a targeted learning framework using TMLE and pseudo-observations to accurately estimate the difference in restricted mean survival time in clinical trials, including sensitivity analysis for censoring.

Contribution

It develops a novel targeted minimum loss estimator for RMST difference and incorporates a copy reference approach for sensitivity analysis in censored data.

Findings

Effective estimation of RMST difference demonstrated on real data

Framework handles right-censoring with sensitivity analysis

Improves accuracy over traditional methods

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.