title: Cluster Effects in a National Dental PBRN restorative study.
abstract: Items in clusters, such as patients of the same clinician or teeth within the same patient, tend to be more similar than items from different groups. This within-group similarity, represented by the intraclass correlation coefficient (ICC), reduces precision, yielding less statistical power and wider confidence intervals, compared with non-clustered samples of the same size. This must be considered in the design of studies including clusters. We present ICC estimates from a study of 7,826 restorations placed in previously unrestored tooth surfaces of 4,672 patients by 222 clinicians in the National Dental Practice-Based Research Network, as a resource for sample size planning in restorative studies. Our findings suggest that magnitudes of ICCs in practice-based research can be substantial. These can have large effects on precision and the power to detect treatment effects. Generally, we found relatively large ICCs for characteristics that are influenced by clinician choice (e.g., 0.36 for rubber dam use). ICCs for outcomes within individual patients, such as tooth surfaces affected by a caries lesion, tended to be smaller (from 0.03 to 0.15), but were still sufficiently large to substantially affect statistical power. Clustering should be taken into account in the design of oral health studies and derivation of statistical power estimates for these studies (ClinicalTrials.gov, NCT00847470).
authors.affiliation: Department of Clinical and Community Sciences, School of Dentistry, University of Alabama at Birmingham, USA. mlitaker@uab.edu
authors.forename: M S
authors.initials: MS
authors.lastname: Litaker
authors.forename: V V
authors.initials: VV
authors.lastname: Gordan
authors.forename: D B
authors.initials: DB
authors.lastname: Rindal
authors.forename: J L
authors.initials: JL
authors.lastname: Fellows
authors.forename: G H
authors.initials: GH
authors.lastname: Gilbert
mesh_terms: D016000:Cluster Analysis; D055812:Community-Based Participatory Research / Q000706:statistics & numerical data*; D003188:Composite Resins; D003722:Dental Alloys; D003731:Dental Caries / Q000145:classification / Q000628:therapy; D003736:Dental Cavity Lining / Q000706:statistics & numerical data; D003764:Dental Materials; D018865:Dental Research / Q000706:statistics & numerical data; D003793:Dental Restoration, Permanent / Q000706:statistics & numerical data*; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D005899:Glass Ionomer Cements; D006801:Humans; D007347:Insurance, Dental / Q000706:statistics & numerical data; D008297:Male; D019239:Practice Patterns, Dentists' / Q000706:statistics & numerical data; D012107:Research Design / Q000706:statistics & numerical data; D016733:Rubber Dams / Q000706:statistics & numerical data; D018401:Sample Size; D014071:Tooth Abnormalities / Q000628:therapy; D014082:Tooth Fractures / Q000628:therapy; D057085:Tooth Wear / Q000628:therapy; D019553:Tooth, Nonvital / Q000628:therapy
chemical_list: D003188:Composite Resins; D003722:Dental Alloys; D003764:Dental Materials; D005899:Glass Ionomer Cements
keywords: cluster trials; dental research; dentists; intraclass correlation coeffiecient; multilevel analysis; research methodology