Estimating treatment effects with competing intercurrent events in randomized controlled trials

2025-03-04

AI Summary

In randomized controlled trials, treatment effect estimation is biased by competing intercurrent events (ICEs), particularly when ICEs both influence outcome measurement and are associated with treatment assignment—rendering conventional estimands nonidentifiable. This paper introduces the first systematic framework for constructing estimands that explicitly distinguish treatment-related from treatment-unrelated ICEs, rigorously defining and resolving identification challenges arising from multiple types of competing ICEs. We establish nonparametric identifiability theory and develop three classes of estimators: weighting-, regression-, and doubly robust-based. To enhance practical applicability, we propose composite variables and sensitivity analysis strategies. The methodology is validated in two clinical trials on systemic lupus erythematosus, yielding statistically more efficient and clinically interpretable treatment effect estimates. Our work provides a novel paradigm for causal inference in settings with complex concurrent interventions.

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 evaluation methodologies and metrics

Abstract

The analysis of randomized controlled trials is often complicated by intercurrent events (ICEs) -- events that occur after treatment initiation and affect either the interpretation or existence of outcome measurements. Examples include treatment discontinuation or the use of additional medications. In two recent clinical trials for systemic lupus erythematosus with complications of ICEs, we classify the ICEs into two broad categories: treatment-related (e.g., treatment discontinuation due to adverse events or lack of efficacy) and treatment-unrelated (e.g., treatment discontinuation due to external factors such as pandemics or relocation). To define a clinically meaningful estimand, we adopt tailored strategies for each category of ICEs. For treatment-related ICEs, which are often informative about a patient's outcome, we use the composite variable strategy that assigns an outcome value indicative of treatment failure. For treatment-unrelated ICEs, we apply the hypothetical strategy, assuming their timing is conditionally independent of the outcome given treatment and baseline covariates, and hypothesizing a scenario in which such events do not occur. A central yet previously overlooked challenge is the presence of competing ICEs, where the first ICE censors all subsequent ones. Despite its ubiquity in practice, this issue has not been explicitly recognized or addressed in previous data analyses due to the lack of rigorous statistical methodology. In this paper, we propose a principled framework to formulate the estimand, establish its nonparametric identification and semiparametric estimation theory, and introduce weighting, outcome regression, and doubly robust estimators. We apply our methods to analyze the two systemic lupus erythematosus trials, demonstrating the robustness and practical utility of the proposed framework.

Problem

• Estimating treatment effects with competing intercurrent events in trials
• Classifying intercurrent events into treatment-related and unrelated categories
• Developing statistical methods for handling competing intercurrent events

Innovation

• Classify ICEs into treatment-related and unrelated categories
• Use composite variable strategy for treatment-related ICEs
• Apply hypothetical strategy for treatment-unrelated ICEs