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.

Highlights

  • In cluster randomised controlled trials, groups of individuals are randomised to minimise the risk of contamination and/or efficiently use limited resources or solve logistic and administrative problems

  • In 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

  • The inadequate analysis and poor reporting of cluster trials published in the United Kingdom (UK) is still happening in recent times, despite the availability of the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines for cluster trials published over a decade ago

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Summary

Introduction

In 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. Randomised controlled trials (iRCTs) are common, but in practice, this trial design may suffer from the potential contamination of outcomes from participants in the trial. The cluster randomised controlled trial (cRCT) design can be used to minimise the risks posed by contamination [2, 3].Other rationales for using a cRCT design are maximisation of limited resources, problems with logistics, and administrative convenience [2]. A cRCT is potentially a more powerful design in handling the above-named issues, with groups of individuals (rather than individuals) randomly allocated to the experimental arms, resulting in outcome data that is clustered. Going forward, for simplicity we have interchangeably used “cluster trials” to mean cRCTs

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