Abstract #300837

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JSM 2003 Abstract #300837
Activity Number: 127
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300837
Title: Inference Methods for Exchangeable Binary Data
Author(s): Catalina Stefanescu*+ and Bruce W. Turnbull
Companies: London Business School and Cornell University
Address: Regents Park, London, NW1 4SA, England
Keywords: exchangeable binary data ; maximum likelihood
Abstract:

Correlated exchangeable data may be encountered in many application areas, such as studies of disease occurrence in families, developmental toxicity experiments, or group randomized trials. When data are clustered, modeling the correlation within groups of observations is necessary for the validity of the statistical inference. This paper investigates inference methods for 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 extend the saturated models to allow the inclusion of covariates. The methodology is illustrated with several applications.

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