Estimand Framework - discussions at JSM 2024

In the Joint Statistical Meetings 2024, there was a session "Global Impact of the ICH E9(R1) Addendum - 5-Year Anniversary for the Trial Estimand Framework". In this session, panel members discussed the global impact of this guidance at its 5-year anniversary. Members from different regulatory agencies and the pharmaceutical industry, including members from the ICH E9(R1) Expert Working Group, reflected on multiple aspects of the impact of the estimand framework and the current stage of its broad implementation across clinical trials, including:

• impact of the estimand framework to regulatory interactions, trial planning, drug approval process and labeling
• examples of estimand framework implementation
• disease-specific regulatory guidance documents using the estimand framework, including future plans
• implementation of the ICH E9(R1) Addendum in clinical trial practice, from start (planning a trial, protocol development) to finish (reporting, communication and dissemination of results) and any remaining challenges
• estimand thinking process and multi-disciplinary collaborations
• estimand-related initiatives and their global impact
• development of statistical methodologies (e.g. missing data and causal inference methods) triggered by the ICH E9(R1) Addendum;
• standardizations efforts that facilitate the implementation of this framework; opportunities for the future.

The ICH E9(R1) guideline has been formally endorsed by major regulatory agencies such as the EMA, FDA, HC, and PMDA. The concepts of estimands and intercurrent events are frequently addressed in regulatory feedback on study protocols and statistical analysis plans. However, these topics have yet to gain traction in disease-specific scientific conferences. For instance, at several conferences and congresses focused on cardiovascular and pulmonary diseases, I observed a notable absence of presentations or abstracts discussing the estimand framework. Within the industry, estimands and intercurrent events are often seen as purely statistical concepts, primarily relevant to statisticians.

Estimand: A precise description of the treatment effect reflecting the clinical question posed by the trial objective. It summarises at a population-level what the outcomes would be in the same patients under different treatment conditions being compared. 

Estimator: A method of analysis to compute an estimate of the estimand using clinical trial data.

Estimate: A numerical value computed by an estimator. 

A question arose regarding the lack of an appropriate estimator for the corresponding estimand. Stephen Ruberg, from the audience, provided an insightful response by quoting John Tukey: 'An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem. The greatest value of a picture is when it forces us to notice what we never expected to see. An approximate answer to the right question is worth far more than a precise answer to the wrong one.' In the context of the estimand framework, it is crucial to ask the right question and define the estimand accurately. While an exact estimator for the proposed estimand may not exist, it can often be reasonably approximated.

The estimand framework has been built into some disease-specific regulatory guidance documents. For example, in FDA's guidance for industry "Graft-versus-Host Diseases: Developing Drugs, Biological Products, and Certain Devices for Prevention or Treatment", the estimand and intercurrent events are extensively discussed and example estimands are provided.