The wood and the trees: estimands in cluster randomised trials.
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a methodological commentary |
| I — Intervention | Not applicable - this is a methodological commentary |
| C — Comparator | Not applicable - this is a methodological commentary |
| O — Outcomes | Not applicable - this is a methodological commentary |
Bottom Line
This methodological commentary discusses the application of estimand frameworks to cluster randomised trials, highlighting challenges such as informative cluster size and different stakeholder perspectives. While not providing clinical evidence for patient care, it addresses important statistical considerations for researchers designing cluster trials. The authors emphasise that different actors (policymakers, cluster managers, patients) may require different estimands, and caution against abandoning careful statistical modelling. For clinicians, this paper is primarily relevant for understanding the methodological underpinnings of cluster trials they may encounter in the literature, rather than providing direct clinical guidance. The work contributes to improving the quality and interpretability of cluster trial reporting, which may ultimately benefit evidence-based practice.
Key Findings
P Value: Not applicable - no hypothesis testing
Effect Size: Not applicable - no quantitative analysis
Primary Outcome: Not applicable - methodological commentary
Nnt Or Sensitivity: Not applicable - no clinical outcomes measured
Confidence Interval: Not applicable - no statistical estimates
Clinical Application
Concepts may inform better trial design but require statistical expertise to implement Relevant for Australian researchers conducting cluster trials, particularly in primary care settings aligned with RACGP research priorities Researchers designing cluster randomised trials rather than clinical populations
Abstract
BACKGROUND: The estimand framework was introduced into guidance on good clinical practice to address a variety of shortcomings and ambiguities in the reporting of trials, including the use of terms such as "intention to treat" and the handling of non-adherence to treatment. The framework was primarily grounded in individually randomised trials, and some thorny issues still cloud understanding of its application to cluster randomised trials. ESTIMANDS IN CLUSTER RANDOMISED TRIALS: This commentary addresses some of the challenges in thinking about the estimands behind cluster randomised trials. These challenges include informative cluster size-the possibility that a treatment effect may be modified by the size of the cluster. In particular, we consider the perspectives of different actors-a population-level decision maker or politician, a cluster manager, and a patient-and examine possible estimands for each, and how they differ. CONCLUSIONS: In the cluster randomised trial context, the estimand framework can be complex to navigate. Different perspectives lead to different estimands. We caution against abandoning careful statistical modelling. This is particularly true in the presence of informative cluster size, where modelling any interaction between cluster size and treatment effect could be useful from a number of perspectives.
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