Abstract #300994

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JSM 2003 Abstract #300994
Activity Number: 96
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300994
Title: A Multistate Stochastic Model for Longitudinal Data with Discrete Outcomes
Author(s): Sujuan Gao*+
Companies: Indiana University-Purdue University Indianapolis
Address: Division of Biostatistics, Indianapolis, IN, 46202,
Keywords: longitudinal data ; discrete outcomes ; Markov model
Abstract:

We consider longitudinal studies where multiple discrete outcomes are collected over time and transitions between these outcomes are of interest. We propose a multistate stochastic model for the analysis of such data. The model includes multiple states with some reversible states and an absorbing state for death. Homogeneous Markov property is often assumed for these multistate models which assume transitional probabilities depend only on the stochastic states the subjects occupy at a prior step. The Markov assumption does not accommodate the correlations among multiple outcomes observed over time from each subjects. We present a multistate model that include subject-specific random effects and discuss issues related to parameter estimation from the maximum likelihood approach. Data from a longitudinal dementia study are used to illustrate our approach.


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