ArticleTrials2026
The wood and the trees: estimands in cluster randomised trials.
Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThe 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.
conclusionsIn 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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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.