JSM 2005 - Toronto

Abstract #303639

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 320
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #303639
Title: Marginalized Transition Models for Longitudinal Categorical Data
Author(s): Keunbaik Lee*+ and Michael Daniels
Companies: University of Florida and University of Florida
Address: 103 GriffinFloyd Hall PO Box 118545, Gainesville, FL, 32611-8545, United States
Keywords: Longitudinal data ; Ordinal data ; Marginalized transition model ; EM
Abstract:

Generalized linear models with serial dependence are commonly used for longitudinal data analysis. Heagerty (2002) suggested marginalized transition models for the analysis of longitudinal binary data. In this paper, we extend his work to accommodate longitudinal ordinal data using cumulative logit model. We also suggest a Bayesian version of marginalized transition model for ordinal longitudinal data. EM algorithms and MCMC methods are developed for estimation. Methods are illustrated on quality-of-life data from a colorectal cancer clinical trial.

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