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Abstract Details

Activity Number: 244
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #306208
Title: A Bayesian Item Response Model for Nonignorable Missing Data in Parkinson Disease Patients
Author(s): Sheng Luo*+ and Xiao Su and Bo He and Barbara Tilley
Companies: 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
Address: 1200 Herman Pressler St, Houston, TX, 77030, United States
Keywords: Multivariate outcomes ; Global treatment effect ; Latent variable ; Markov chain Monte Carlo ; Clinical trial ; Mixed model
Abstract:

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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