부산대학교 도서관

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1600-0528.2006.00307.x Byline: Hae-Young Kim (1), John S. Preisser (2), R. Gary Rozier (3), Jayasanker V. Valiyaparambil (3) Keywords: cluster trials; dental survey incentives; group-randomized trials; response rates; school-based survey research Abstract: Abstract - Objectives: Many dental studies have assessed the effectiveness of community- or group-based interventions such as community water fluoridation. These cluster trials, of which group-randomized trials (GRTs) are one type, have design and analysis considerations not found in studies with randomization of treatments to individuals (randomized controlled trials, RCTs). The purpose of this paper is to review analytic methods used for the analysis of binary outcomes from cluster trials and to illustrate these concepts and analytical methods using a school-based GRT. Methods: We examine characteristics of GRTs including intra-class correlation (ICC), their most distinctive feature, and review analytical methods for GRTs including group-level analysis, adjusted chi-square test and multivariable analysis (mixed effect models and generalized estimating equations) for correlated binary data. We consider two- and three-level modeling of data from a cross-sectional cluster design. We apply the concepts reviewed using a GRT designed to determine the effect of incentives on response rates in a school-based dental study. We compare the results of analyses using methods for correlated binary data with those from traditional methods that do not account for ICC. Results: Application of traditional analytic methods to the dental GRT used as an example for this paper led to a substantial overstatement of the effectiveness of the intervention. Conclusions: Ignoring the ICC among members of the same group in the analysis of public health intervention studies can lead to erroneous conclusions where groups are the unit of assignment. Special consideration is needed in the analysis of data from these cluster trials. Randomization of treatments to groups also should receive more consideration in the design of cluster trials in dental public health. Author Affiliation: (1)Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea (2)Department of Biostatistics, School of Public Health University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (3)Department of Health Policy and Administration, School of Public Health University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Article History: Submitted 13 June 2005; accepted 28 March 2006 Article note: R. Gary Rozier, Department of Health Policy and Administration, The University of North Carolina at Chapel Hill 1105F McGavran-Greenberg Hall, CB No. 7411 Chapel Hill, NC 27599-7400, USA, Tel: 919 966 7388, Fax: 919 966 6961, e-mail: gary_rozier@unc.edu