Clustering by Health Professionals in Individually Randomised Controlled Trials

<jats:p>Purpose: The aim of this study was to investigate the prevalence of clustering by health professionals in individually randomised controlled trials (iRCT), and its adjustment in both the sample size calculation estimates and the analysis of the data collected in iRCT (that is, trials that randomise individuals only). As a result, cluster randomised controlled trials will not be the part of this review study. Additionally, the authors aimed to discover the prevalence of the various forms of clustering in iRCT. Methods: iRCT, in which the intervention was delivered by a health professional, were electronically searched in three medical journals. The dates searched were from 1st January 2000–31st August 2009. The retrieved trials were then screened to exclude those with complex designs and trials with more than two parallel arms. The selected trials were then fully reviewed for the presence of clustering effects and any corresponding adjustment. Data about the sample size calculation in the selected trials were also included. A basic form was generated for the purpose of data extraction from each of the selected trials. Results: Of the 130 iRCT reviewed, clustering of outcomes was present in 127 (98%) trials. Only 61 trials (47%) had adjusted for the clustering effects in their design and analysis, while 53% of the trials had ignored the clustering effect, and hence no adjustment had been made in the trial design or analysis. Regarding the various forms of clustering, clustering by centre in multicentre trials was found in 79 trials (60%), followed by natural clustering in 26 trials (20%), and clustering imposed by the design of the study in 23 trials (18%). Conclusion: Potential clustering of outcomes exists in almost all iRCT; however, this review found that &lt;50% of iRCT took clustering into account and adjusted the sample size calculation and statistical analysis of this data for clustering. Almost half of the reviewed iRCT ignored the clustering effect. As a result, inaccurate and nongeneralisable results could have been generated.</jats:p>