title: Likelihood inference for exchangeable binary data with varying cluster sizes.
abstract: This article investigates maximum likelihood estimation with saturated and unsaturated models for correlated exchangeable binary data, when a sample of independent clusters of varying sizes is available. We discuss various parameterizations of these models, and propose using the EM algorithm to obtain maximum likelihood estimates. The methodology is illustrated by applications to a study of familial disease aggregation and to the design of a proposed group randomized cancer prevention trial.
authors.affiliation: London Business School, Regent's Park, London NW1 4SA, UK.
authors.forename: Catalina
authors.initials: C
authors.lastname: Stefanescu
authors.forename: Bruce W
authors.initials: BW
authors.lastname: Turnbull
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D019587:Dietary Supplements; D006801:Humans; D016013:Likelihood Functions; D009369:Neoplasms / Q000453:epidemiology / Q000517:prevention & control; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology / Q000235:genetics; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D018401:Sample Size; D012643:Selenium
chemical_list: D012643:Selenium