Trial reportBMJ quality & safety2018
Ethical implications of excessive cluster sizes in cluster randomised trials.
Trial report in BMJ quality & safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of sample size estimation accuracy on power in malaria cluster randomised trials measuring epidemiological outcomes.BMC medical research methodology · 2024Pooled it
- Variance partitioning reveals contrasting random effect contributions to the density and species composition of malaria-transmitting mosquitoes in western Burkina Faso.Parasites & vectors · 2026Article
- Compassionate mind training as school-based social and emotional learning: extension to younger pupils and effects of programme dosage.Frontiers in psychology · 2026Article
- Enhancing insight into regional differences: hierarchical linear models in multiregional clinical trials.BMC medical research methodology · 2025Article
- Establishing the safety of selective digestive decontamination within the ICU population: a bridge too far?Trials · 2023Article
- Article
- Empowering local research ethics review of antibacterial mass administration research.Infectious diseases of poverty · 2022Review
- Article
- Ethical issues in the design and conduct of stepped-wedge cluster randomized trials in low-resource settings.Trials · 2019Article
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5 authors.
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Abstract
The cluster randomised trial (CRT) is commonly used in healthcare research. It is the gold-standard study design for evaluating healthcare policy interventions. A key characteristic of this design is that as more participants are included, in a fixed number of clusters, the increase in achievable power will level off. CRTs with cluster sizes that exceed the point of levelling-off will have excessive numbers of participants, even if they do not achieve nominal levels of power. Excessively large cluster sizes may have ethical implications due to exposing trial participants unnecessarily to the burdens of both participating in the trial and the potential risks of harm associated with the intervention. We explore these issues through the use of two case studies. Where data are routinely collected, available at minimum cost and the intervention poses low risk, the ethical implications of excessively large cluster sizes are likely to be low (case study 1). However, to maximise the social benefit of the study, identification of excessive cluster sizes can allow for prespecified and fully powered secondary analyses. In the second case study, while there is no burden through trial participation (because the outcome data are routinely collected and non-identifiable), the intervention might be considered to pose some indirect risk to patients and risks to the healthcare workers. In this case study it is therefore important that the inclusion of excessively large cluster sizes is justifiable on other grounds (perhaps to show sustainability). In any randomised controlled trial, including evaluations of health policy interventions, it is important to minimise the burdens and risks to participants. Funders, researchers and research ethics committees should be aware of the ethical issues of excessively large cluster sizes in cluster trials.
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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 OpenQuestion graph.