Abstract Details
Activity Number:
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177
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract #314961
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Title:
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Semiparametric Random-Effect Models for Panel Count Data with Informative Observation Times
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Author(s):
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Yang Li* and Yanqing Sun
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Companies:
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The University of North Carolina at Charlotte and The University of North Carolina at Charlotte
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Keywords:
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Estimating equation ;
Informative censoring ;
Informative observation process ;
Panel count data ;
Semiparametric regression
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Abstract:
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Panel count data frequently arise from many fields such as medical follow-up studies concerning certain recurrent events. In such situations, the responses are recorded only at discrete observation times. Most existing approaches on panel count data analysis assumed that the observation or follow-up times are independent of the recurrent event process either completely or given some covariates. We present a joint analysis approach in which the possible correlations among the responses, observation and follow-up times can be characterized by time-dependent random effects. Estimating equations are developed for the parameter estimation and the resulted estimates are shown to be consistent and asymptotically normal. A simulation study is conducted to assess the finite sample performance of the approach and the method is applied to data arising from a skin cancer study.
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Authors who are presenting talks have a * after their name.
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