Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomes

Fuente: arXiv
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Auteurs principaux: Fang, Yixin, Jin, Man
Format: Preprint
Publié: 2025
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author Fang, Yixin
Jin, Man
author_facet Fang, Yixin
Jin, Man
contents The ICH E9(R1) guideline presents a framework of estimand for clinical trials, proposes five strategies for handling intercurrent events (ICEs), and provides a comprehensive discussion and many real-life clinical examples for quantitative outcomes and categorical outcomes. However, in ICH E9(R1) the discussion is lacking for time-to-event (TTE) outcomes. In this paper, we discuss how to define estimands and how to handle ICEs for clinical trials with TTE outcomes. Specifically, we discuss six ICE handling strategies, including those five strategies proposed by ICH E9(R1) and a new strategy, the competing-risk strategy. Compared with ICH E9(R1), the novelty of this paper is three-fold: (1) the estimands are defined in terms of potential outcomes, (2) the methods can utilize time-dependent covariates straightforwardly, and (3) the efficient estimators are discussed accordingly.
format Preprint
id arxiv_https___arxiv_org_abs_2510_15000
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomes
Fang, Yixin
Jin, Man
Methodology
Machine Learning
The ICH E9(R1) guideline presents a framework of estimand for clinical trials, proposes five strategies for handling intercurrent events (ICEs), and provides a comprehensive discussion and many real-life clinical examples for quantitative outcomes and categorical outcomes. However, in ICH E9(R1) the discussion is lacking for time-to-event (TTE) outcomes. In this paper, we discuss how to define estimands and how to handle ICEs for clinical trials with TTE outcomes. Specifically, we discuss six ICE handling strategies, including those five strategies proposed by ICH E9(R1) and a new strategy, the competing-risk strategy. Compared with ICH E9(R1), the novelty of this paper is three-fold: (1) the estimands are defined in terms of potential outcomes, (2) the methods can utilize time-dependent covariates straightforwardly, and (3) the efficient estimators are discussed accordingly.
title Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomes
topic Methodology
Machine Learning
url https://arxiv.org/abs/2510.15000