Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomes
Yixin Fang, Man Jin
TL;DR
The paper tackles the challenge of defining and estimating treatment effects in clinical trials with time-to-event outcomes under intercurrent events (ICEs). It extends the ICH E9(R1) estimand framework by introducing six ICE handling strategies (the original five plus a competing-risk strategy), and defines estimands using potential outcomes, enabling clear interpretation in the presence of censoring and time-dependent covariates. Estimation is grounded in targeted learning (TMLE) with practical guidance for implementation (ltmle, survtmle) and sensitivity analyses via multiple imputation under CAR, CR, and J2R assumptions. The work emphasizes the integration of robust estimators, transparent ICE handling, and flexible data structures, providing a roadmap for applying estimands in TTE trials while highlighting areas for further methodological development and empirical illustration.
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.