Abstract Details
Activity Number:
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483
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #310228 |
Title:
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Maximum-Likelihood Estimation of Marginally Specified Joint Models for the Mean and the Correlation for Clustered Binary Outcomes
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Author(s):
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Bahjat Qaqish*+
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Companies:
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UNCCH
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Keywords:
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marginally-specified models ;
Alternating logistic regression ;
GEE ;
Clustered binary outcomes
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Abstract:
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We describe a method for the estimation of marginally-specified regression parameters in the mean and the pairwise odds-ratio structure of clustered binary outcomes. The method entails maximum-likelihood estimation within a special parametric family. The information matrix is intractable but we present three consistent estimators that perform well in simulation studies. We show that asymptotic bias due to misspecification of the parametric family is generally small. We also present an application to a longitudinal study.
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Authors who are presenting talks have a * after their name.
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