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Abstract Details
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Activity Number:
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150
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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| Abstract - #300010 |
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Title:
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Regression Analysis of Longitudinal Data with Dependent Observation Process and Application to Medical Cost Data
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Author(s):
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"Tony" Jianguo Sun*+ and Liang Zhu
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Companies:
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University of Missouri at Columbia and St. Jude Children's Research Hospital
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Address:
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134E Middlebush Hall, Columbia, MO, 65211-6100,
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Keywords:
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Counting processes ;
Latent variable model ;
Longitudinal data analysis
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Abstract:
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Longitudinal data analysis is one of the most discussed and applied areas in statistics and a great deal of literature has been developed for it. However, most of the existing literature focus on the situation where observation times are fixed or can be treated as fixed constants. This paper considers the situation where these observation times may be random variables and more importantly, they may be related to the underlying longitudinal variable or process of interest. For the problem, we present a joint modeling approach and an estimating equation-based procedure is developed for estimation of possibly time-varying regression parameters. The methodology is applied to a set of medical cost data from an acute myeloid leukemia trial.
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