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Abstract Details
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 - #301558 |
Title:
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Analysis of Longitudinal Data with Non-Random Missingness Using Shared Random Effects Models
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Author(s):
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Xiaoyun Li*+ and Stuart Lipsitz and Dipankar Bandyopadhyay and Geert Molenberghs and Debajyoti Sinha
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Companies:
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Merck & Co., Inc. and Brigham and Women's Hospital and Medical University of South Carolina and Universiteit Hasselt/Katholieke Universiteit Leuven and Florida State University
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Address:
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131, Church Road, Apt# 1J, North Wales, PA, 19454, United States
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Keywords:
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Bridge density ;
Logistic link ;
Longitudinal data ;
Non-random missingness ;
Shared random effects
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Abstract:
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Incomplete data is common in longitudinal studies, owing to subjects missing one or more follow up visits. When the missing-data mechanism depends on the outcome of interest, inferences that only use the complete data will no longer be valid. Shared random-effects models that uses latent variables to link the binary longitudinal response to the missing-data mechanism have been proposed, within a Gaussian framework. However, such models only describe the conditional model of the binary response given random effects and do not yield a closed-form marginal model for either the binary longitudinal response or the missing-data mechanism. We propose a shared random-effects model that, unlike the existing models, allows to preserve a logistic regression form for, at the same time, the marginal probability of the binary response, the missing-data indicator, and the conditional probability of the
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