JSM 2005 Online Program

Abstract #303277

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	102
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303277
Title:	Bayesian Inference of Hepatotoxicity
Author(s):	Qianqiu Li*+ and Xiaotong Shen and Dennis Pearl
Companies:	The Ohio State University and University of Minnesota and The Ohio State University
Address:	5273 Fairlane Dr, Powell, OH, 43065, United States
Keywords:	Hepatotoxicity ; Drug concentration ; Mean reversion ; MCMC ; State space
Abstract:	Hepatotoxicity (liver damage) is a common problem in drug-treatment trials, but observed only indirectly through biomarkers measured in the blood. This creates the need to infer a patient's unobserved liver function dynamically using blood tests and other patient baseline characteristics. Major challenging issues include high dimensionality, presence of missing observations, irregular time observation points between patients, and noise involved in measurement and biological processes. In this presentation, we present a class of multivariate Bayesian dynamic stochastic models for detecting and forecasting changes in a patient's liver function without and with drug. In addition, we will present simulation and data analysis results from a clinical toxicity study. The numerical results support utility of the methodology.

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