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.

翻译：国际人用药品注册技术协调会于2017年提出的估计目标框架为制药行业带来了根本性变革。该框架通过治疗方案、终点指标和伴发事件等属性，清晰界定了临床问题中治疗效应的精确定义与估计方式。然而，关于该框架的阐述通常以文本形式呈现，可能存在不同的解读视角。本文旨在基于其底层理论——即基于潜在结果的因果推断框架——对估计目标框架进行解读。通过因果推断框架给出了估计目标的统计学起源与数学表达式，并将所有属性转化为统计学术语。阐述了国际人用药品注册技术协调会提出的五种伴发事件分析策略如何融入估计目标的统计表达式，同时提出了一种新的伴发事件分析策略。基于估计目标的统计表达式，比较并讨论了目标人群与分析集在估计目标框架中的作用。本文建议持续深入研究估计目标框架背后的因果推断理论，通过提升方法学的可理解性与可用性来完善该框架。