title: Methods for the statistical analysis of binary data in split-cluster designs.
abstract: Split-cluster designs are frequently used in the health sciences when naturally occurring clusters such as multiple sites or organs in the same subject are assigned to different treatments. However, statistical methods for the analysis of binary data arising from such designs are not well developed. The purpose of this article is to propose and evaluate a new procedure for testing the equality of event rates in a design dividing each of k clusters into two segments having multiple sites (e.g., teeth, lesions). The test statistic proposed is a generalization of a previously published procedure based on adjusting the standard Pearson chi-square statistic, but can also be derived as a score test using the approach of generalized estimating equations.
authors.affiliation: Department of Epidemiology and Biostatistics, University of Western Ontario, London, Ontario N6A 5C1, Canada. donner@biostats.uwo.ca
authors.forename: Allan
authors.initials: A
authors.lastname: Donner
authors.forename: Neil
authors.initials: N
authors.lastname: Klar
authors.forename: Guangyong
authors.initials: G
authors.lastname: Zou
mesh_terms: D000891:Anti-Infective Agents, Local / Q000627:therapeutic use; D001699:Biometry; D016009:Chi-Square Distribution; D002710:Chlorhexidine / Q000627:therapeutic use; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003625:Data Collection / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005891:Gingivitis / Q000188:drug therapy; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D000891:Anti-Infective Agents, Local; D002710:Chlorhexidine