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

2025-10-15

AI Summary

The ICH E9(R1) guideline lacks systematic guidance on defining causal estimation targets for time-to-event (TTE) endpoints and handling intercurrent events (ICEs). Method: Building upon the potential outcomes framework, we develop a causal estimation target system specifically for TTE endpoints and propose six ICE-handling strategies—including competing risks modeling—while explicitly incorporating time-varying covariates into the target definition. We further design identifiable, efficient, and causally interpretable estimators by integrating counting process theory with modern survival analysis techniques. Contribution/Results: This work fills a critical regulatory and methodological gap, providing a theoretically rigorous yet practically implementable statistical framework for TTE clinical trials. The framework ensures valid causal interpretation, supports principled benefit–risk assessment, and enhances regulatory alignment without compromising statistical efficiency or conceptual clarity.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation

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

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.

Problem

• Extending estimand framework to time-to-event outcomes
• Proposing six strategies for handling intercurrent events
• Introducing novel potential outcomes-based estimand definitions

Innovation

• Defines estimands using potential outcomes framework
• Utilizes time-dependent covariates in methods
• Discusses efficient estimators for time-to-event outcomes