Interpret the estimand framework from a causal inference perspective

TL;DR

This paper interprets the estimand framework from a causal inference perspective, translating its attributes into statistical terms and analyzing strategies for intercurrent events to enhance clarity and methodology in clinical research.

Contribution

It provides a causal inference-based interpretation of the estimand framework, introduces a new strategy for intercurrent events, and discusses methodological improvements.

Findings

Causal inference offers a clear statistical foundation for the estimand framework.

Five strategies for intercurrent events are incorporated into the estimand's formula.

A new strategy for analyzing intercurrent events is proposed.

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. We describe how five strategies proposed by ICH to analyze intercurrent events are incorporated in the statistical formula of an estimand, and we also suggest a new strategy to analyze intercurrent events. 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 studying causal inference theories behind the estimand framework and improving the estimand framework with greater methodological comprehensibility and availability.