This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 128
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #308819
Title: Multivariate Longitudinal Bayesian Imputation for Global Statistical Test, with Applications to Parkinson's Disease
Author(s): Sheng Luo*+ and Andrew Lawson and Jordan J. Elm and Barbara C. Tilley
Companies: The University of Texas School of Public Health and Medical University of South Carolina and Medical University of South Carolina and The University of Texas Health Science Center at Houston
Address: 1200 Herman Pressler St, Houston, TX, 77030,
Keywords: Multivariate longitudinal outcomes ; Global statistical test ; Item-response theory ; Mixed model ; Bayesian

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, the Global Statistical Test (GST) is frequently used. In Parkinson's Disease clinical trials, missing values often occur due to various reasons. In this article, we discuss the Bayesian imputation approaches using item response theory and apply it to GST. The inference is conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in WinBUGS. Simulations are used to validate our Bayesian methods and assess its frequentist properties. Our methods are motivated by, and applied to a long-term PD clinical trial.

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