Evidence map›Paper›PMID 35120567›Full record

ReviewTrials2022

Statistical analysis of publicly funded cluster randomised controlled trials: a review of the National Institute for Health Research Journals Library.

Bright C Offorha, Stephen J Walters, Richard M Jacques

Registry-linked trialAbstract readReview
In one paragraph

Review in Trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06567340 (StratCare Trial 2), which is not on this map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT06567340 narecruitingnot on this mapstarted 2025, after this paper: background citation

StratCare Trial 2: Evaluating the Clinical and Cost-effectiveness of AI-driven Stratified Care for Depression

TypeinterventionalSponsorRotherham Doncaster and South Humber NHS Foundation TrustRan2025 to 2027Enrolled1,252ConditionsDepressionArmsStratCare
3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. On the mixed-model analysis of covariance in cluster-randomized trials.Statistical science : a review journal of the Institute of Mathematical Statistics · 2026
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bright C OfforhaSchool of Health and Related Research, The University of Sheffield, Sheffield, UK. bcofforha1@sheffield.ac.uk.ORCID http://orcid.org/0000-0003-2489-3969
Stephen J WaltersSchool of Health and Related Research, The University of Sheffield, Sheffield, UK.ORCID https://orcid.org/0000-0001-9000-8126
Richard M JacquesSchool of Health and Related Research, The University of Sheffield, Sheffield, UK.ORCID https://orcid.org/0000-0001-6710-5403

Funding

National Institute for Health Research NF-SI-0617-10012Tertiary Education Trust Fund TETF/ES/UNIV/UTURU/TSA/2019
6 · The paper itself

Abstract

backgroundIn cluster randomised controlled trials (cRCTs), groups of individuals (rather than individuals) are randomised to minimise the risk of contamination and/or efficiently use limited resources or solve logistic and administrative problems. A major concern in the primary analysis of cRCT is the use of appropriate statistical methods to account for correlation among outcomes from a particular group/cluster. This review aimed to investigate the statistical methods used in practice for analysing the primary outcomes in publicly funded cluster randomised controlled trials, adherence to the CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines for cRCTs and the recruitment abilities of the cluster trials design.

methodsWe manually searched the United Kingdom's National Institute for Health Research (NIHR) online Journals Library, from 1 January 1997 to 15 July 2021 chronologically for reports of cRCTs. Information on the statistical methods used in the primary analyses was extracted. One reviewer conducted the search and extraction while the two other independent reviewers supervised and validated 25% of the total trials reviewed.

resultsA total of 1942 reports, published online in the NIHR Journals Library were screened for eligibility, 118 reports of cRCTs met the initial inclusion criteria, of these 79 reports containing the results of 86 trials with 100 primary outcomes analysed were finally included. Two primary outcomes were analysed at the cluster-level using a generalized linear model. At the individual-level, the generalized linear mixed model was the most used statistical method (80%, 80/100), followed by regression with robust standard errors (7%) then generalized estimating equations (6%). Ninety-five percent (95/100) of the primary outcomes in the trials were analysed with appropriate statistical methods that accounted for clustering while 5% were not. The mean observed intracluster correlation coefficient (ICC) was 0.06 (SD, 0.12; range, - 0.02 to 0.63), and the median value was 0.02 (IQR, 0.001-0.060), although 42% of the observed ICCs for the analysed primary outcomes were not reported.

conclusionsIn practice, most of the publicly funded cluster trials adjusted for clustering using appropriate statistical method(s), with most of the primary analyses done at the individual level using generalized linear mixed models. However, the inadequate analysis and poor reporting of cluster trials published in the UK is still happening in recent times, despite the availability of the CONSORT reporting guidelines for cluster trials published over a decade ago.

Indexed as

Periodicals as TopicCluster AnalysisHumansLinear ModelsRandomized Controlled Trials as TopicResearch DesignResearch ReportClusteringCluster randomised controlled trialsCONSORTIntracluster correlation coefficientRecruitmentStatistical methods

Identifiers

PMID35120567
PMCPMC8817506

What OpenQuestion holds

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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.