The Estimand Framework and Causal Inference: Complementary not Competing Paradigms

TL;DR

This paper explores the relationship between the ICH E9 (R1) estimand framework and causal inference, showing they are complementary tools that improve clarity and robustness in clinical trial design and analysis.

Contribution

It clarifies how the estimand framework and causal inference can be integrated, highlighting their similarities, differences, and complementary roles in clinical trials.

Findings

Both frameworks define population-based effect summaries.

The estimand framework is more accessible for communication.

Causal inference provides precise mathematical assumptions.

Abstract

The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical trials. However, it is unclear how the new framework relates to causal inference, as both approaches appear to define what is being estimated and have a quantity labelled an estimand. Using illustrative examples, we show that both approaches can be used to define a population-based summary of an effect on an outcome for a specified population and highlight the similarities and differences between these approaches. We demonstrate that the ICH E9 (R1) estimand framework offers a descriptive, structured approach that is more accessible to non-mathematicians, facilitating clearer communication of trial objectives and results. We then contrast this with the causal inference framework, which provides a mathematically precise definition of an estimand, and allows the explicit articulation of assumptions through tools such as causal graphs. Despite these differences, the two paradigms should be viewed as complementary rather than competing. The combined use of both approaches enhances the ability to communicate what is being estimated. We encourage those familiar with one framework to appreciate the concepts of the other to strengthen the robustness and clarity of clinical trial design, analysis, and interpretation.