JSM 2004 - Toronto

Abstract #301361

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Activity Number: 163
Type: Contributed
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #301361
Title: Generalized Estimating Equations Analysis of Correlated Multinomial Data Using a New Class of Working Correlation Structures
Author(s): Fangyi Luo*+ and Jorge G. Morel
Companies: Procter & Gamble Company and Procter & Gamble Company
Address: 11450 Grooms Rd., Cincinnati, OH, 45242,
Keywords: GEE ; correlated data ; categorical analysis ; working correlation structure
Abstract:

Generalized Estimating equations (GEE) methodology has been used in the analysis of longitudinal or clustered categorical response data in clinical trials. GEE requires the specification of a working correlation matrix for the repeated measurements within a subject. A new class of working correlation structures for correlated multinomial data is proposed in this paper and incorporated into the GEE analysis using a One Step Gauss-Newton (OSGN) procedure. Under mild regularity conditions and assuming the link function of the response probabilities is correctly specified, the OSGN procedure provides consistent and asymptotically normal estimates of the regression parameters, and consistent estimates of the intracluster correlation parameters. The proposed method is illustrated with a clinical trial example and compared to GEE analysis using independent and exchangeable working correlation structures.

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