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Abstract Details
Activity Number:
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244
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #306208 |
Title:
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A Bayesian Item Response Model for Nonignorable Missing Data in Parkinson Disease Patients
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Author(s):
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Sheng Luo*+ and Xiao Su and Bo He and Barbara Tilley
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Companies:
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The University of Texas at Houston and The University of Texas at Houston and The University of Texas at Houston and The University of Texas at Houston
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Address:
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1200 Herman Pressler St, Houston, TX, 77030, United States
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Keywords:
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Multivariate outcomes ;
Global treatment effect ;
Latent variable ;
Markov chain Monte Carlo ;
Clinical trial ;
Mixed model
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Abstract:
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Parkinson's disease (PD) is multidimensional and is quantified by multiple measurements of mixed types (i.e., continuous, categorical). To assess a treatment's global impact, an item response model is frequently used. In PD clinical trials, missing values often occur due to various reasons. In this paper, we discuss the Bayesian approach for analyzing item response models with nonignorable missing data. Our methods are motivated by, and applied to a long-term PD clinical trial.
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Authors who are presenting talks have a * after their name.
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