Analysis of article using Artificial Intelligence tools
|Author||Offorha B.C.; Walters S.J.; Jacques R.M.|
|Title||Statistical analysis of publicly funded cluster randomised controlled trials: a review of the National Institute for Health Research Journals Library|
Offorha B.C.; Walters S.J.; Jacques R.M. Statistical analysis of publicly funded cluster randomised controlled trials: a review of the National Institute for Health Research Journals Library,Trials 23 1
|Keywords||Cluster Analysis; Humans; Linear Models; Periodicals as Topic; Randomized Controlled Trials as Topic; Research Design; Research Report; controlled study; correlation coefficient; eligibility; extraction; human; medical research; mixed cell culture; outcome assessment; practice guideline; randomized controlled trial; review; United Kingdom; cluster analysis; methodology; publication; randomized controlled trial (topic); research; statistical model
|Link to article|| https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124174527&doi=10.1186%2fs13063-022-06025-1&partnerID=40&md5=f86b438787f98534bbcb1966ef07d12e
|Abstract||Background: 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. 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. Methods: We 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. Results: A 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. Conclusions: 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. 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. © 2022, The Author(s).