Abstract Details
Activity Number:
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304
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #307604 |
Title:
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Markov-Dependent Models for Correlated Binary Responses
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Author(s):
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Forrest Crawford*+ and Daniel Zelterman
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Companies:
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Yale University and Yale University Biostatistics
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Keywords:
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Correlated outcomes ;
binary data ;
dependent data ;
Markov process ;
Poisson
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Abstract:
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Methods for analysis of correlated binary data often suffer from analytic intractability, problems with fitting, restrictive assumptions, or difficulty of interpretation of inferred parameters. In this paper, we establish a correspondence between Markov arrival processes and sums of dependent Bernoulli random variables using a technique called "probabilistic embedding". Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, incorporates ascertainment bias in a natural way, and dramatically simplifies likelihood-based inference. We show how incorporate cluster-specific covariates in a regression setting and apply our method to a dataset of familial chronic obstructive pulmonary disease incidence.
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Authors who are presenting talks have a * after their name.
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