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Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues
Author(s) -
N. Pandis,
Tanya Walsh,
Argy Polychronopoulou,
Theodore Eliades
Publication year - 2012
Publication title -
european journal of orthodontics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.252
H-Index - 84
eISSN - 1460-2210
pISSN - 0141-5387
DOI - 10.1093/ejo/cjs072
Subject(s) - crts , cluster (spacecraft) , randomized controlled trial , sample size determination , randomization , cluster randomised controlled trial , sample (material) , computer science , data mining , medicine , statistics , mathematics , surgery , computer graphics (images) , chemistry , chromatography , programming language
Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).

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