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The wood and the trees: estimands in cluster randomised trials
Richard Hooper1, Karla Hemming2, Fan Li3
1Wolfson Institute of Population Health, Queen Mary University of London, London, UK. r.l.hooper@qmul.ac.uk.
Trials
|April 21, 2026
Summary
The estimand framework in cluster randomized trials is complex. Different perspectives, like those of patients or policymakers, yield varied estimands, especially with informative cluster size, requiring careful statistical modeling.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Health Research Methods
Background:
- The estimand framework aims to clarify trial reporting, addressing ambiguities in terms like "intention to treat" and treatment non-adherence.
- Existing guidance is largely based on individually randomized trials, leaving complexities in cluster randomized trials unresolved.
Purpose of the Study:
- To explore the challenges of defining estimands within cluster randomized trials.
- To examine how different stakeholder perspectives (e.g., policymakers, cluster managers, patients) influence estimand selection.
Main Methods:
- This commentary discusses conceptual challenges in applying the estimand framework to cluster randomized trials.
- It analyzes potential estimands from the viewpoints of various actors involved in healthcare decision-making.
Main Results:
- The application of the estimand framework in cluster randomized trials presents significant complexities.
- Informative cluster size, where cluster size modifies treatment effects, is a key challenge.
- Different perspectives naturally lead to different, valid estimands.
Conclusions:
- Navigating the estimand framework in cluster randomized trials requires careful consideration of diverse viewpoints.
- Abandoning rigorous statistical modeling is ill-advised, particularly when informative cluster size is present.
- Modeling interactions between cluster size and treatment effects can offer valuable insights.
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