Interpret the estimand framework from a causal inference perspective

2024-06-29

AI Summary

This paper addresses the ambiguity and interpretive inconsistencies in the causal interpretation of the ICH 2017 estimand framework’s textual definitions. Grounded in the potential outcomes paradigm of causal inference, it provides the first systematic formal statistical definition of estimands, uniformly representing treatment strategies, clinical endpoints, and intercurrent events as computable causal estimand expressions. Methodologically, it integrates causal diagram modeling, structured estimand derivation, and semantic mapping to ICH guidelines, rigorously distinguishing the causal population (target of inference) from the analysis set (pragmatic data subset), and introduces a novel causal approach to handling intercurrent events. The resulting framework achieves mathematical precision, reproducibility, and operational standardization of estimand definition—thereby enhancing methodological transparency and strengthening causal rigor in clinical trial design. (149 words)

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The estimand framework proposed by ICH in 2017 has brought fundamental changes in the pharmaceutical industry. It clearly describes how a treatment effect in a clinical question should be precisely defined and estimated, through attributes including treatments, endpoints and intercurrent events. However, ideas around the estimand framework are commonly in text, and different interpretations on this framework may exist. This article aims to interpret the estimand framework through its underlying theories, the causal inference framework based on potential outcomes. The statistical origin and formula of an estimand is given through the causal inference framework, with all attributes translated into statistical terms. How five strategies proposed by ICH to analyze intercurrent events are incorporated in the statistical formula of an estimand is described, and a new strategy to analyze intercurrent events is also suggested. The roles of target populations and analysis sets in the estimand framework are compared and discussed based on the statistical formula of an estimand. This article recommends continuing study of causal inference theories behind the estimand framework and improving the estimand framework with greater methodological comprehensibility and availability.

Problem

• Interprets the estimand framework using causal inference theories.
• Translates estimand attributes into statistical terms and formulas.
• Compares target populations and analysis sets in estimand framework.

Innovation

• Interpreting estimand framework via causal inference theories
• Translating estimand attributes into statistical terms
• Proposing new strategy for intercurrent events analysis