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Abstract Details
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Activity Number:
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461
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #301502 |
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Title:
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Analysis of Longitudinal Binary Data with Nonignorable Dropout Using Shared Parameter Models
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Author(s):
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Myungok Lee*+ and Keunbaik Lee
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Companies:
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Louisiana State University and Louisiana State University
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Address:
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Health Sciences Center, New Orleans, LA, 70112,
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Keywords:
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Generalized linear models ;
Marginalized transition ;
Shared parameter models ;
Fisher-scoring
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Abstract:
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In longitudinal studies investigators frequently have to assess and address potential biases introduced by missing data. This paper proposes new methods for modeling longitudinal binary data with nonignorable dropout using maginalized transition models and shared parameter models. Random effects are introduced for both serial dependence of outcomes and nonignorable missingness. Fisher-scoring and Quasi-Newton algorithms are developed for parameter estimation. Methods are illustrated with a real data set.
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