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Cluster :  Health Applications

Session Information  : Tuesday Nov 03, 08:00 - 09:30

Title:  Medical Decision Making in Cancer Care
Chair: Christine Barnett,University of Michigan, 1205 Beal Ave., Ann Arbor, United States of America, clbarnet@umich.edu

Abstract Details

Title: Predictive Modeling for Optimal Design of Cancer Detection Protocols
 Presenting Author: Selin Merdan,University of Michigan, 1205 Beal Avenue, Ann Arbor MI 48109, United States of America, smerdan@umich.edu
 Co-Author: Brian Denton,Associate Professor, University of Michigan, 1205 Beal Ave, Ann Arbor, United States of America, btdenton@umich.edu
 
Abstract: Diagnosis of chronic diseases often involves expensive and invasive tests and procedures. Predictive models can play an important role in determining the optimal diagnostic protocol based on individual patient risk factors. We discuss an approach for developing predictive models using clinical observational data that suffers from common sources of bias such as low disease prevalence and missing data. We illustrate the use of these models for optimization of prostate cancer diagnostic protocols.
  
Title: Model-based Calibration for Natural History Modeling
 Presenting Author: Jing Voon Chen,University of Southern California, Epstein Dept of Indus & Sys Eng, Los Angeles CA, United States of America, jingvooc@usc.edu
 Co-Author: Julie Higle,Professor And Chair, University of Southern California, Epstein Dept of Indus & Sys Eng, Los Angeles CA 90089, United States of America, higle@usc.edu
 
Abstract: A natural history (NH) model often requires calibration of unobservable model parameters to fit observed data. Uncertainty in the data and in the calibrated parameters impacts confidence in the optimal decision. We propose a method for model-based calibration that is resilient to these uncertainties, especially for comparative analyses of disease screening or treatment strategies. Illustrative examples and sensitivity analyses will be discussed.
  
Title: Assessment of Individualized Human Papillomavirus (HPV) Vaccination Strategies
 Presenting Author: Fan Wang,University of Arkansas, 4207 Bell Engineering Center, Fayetteville, United States of America, fxw005@email.uark.edu
 Co-Author: Shengfan Zhang,Assistant Professor, University of Arkansas, 4207 Bell Engineering Center, Fayetteville, United States of America, shengfan@uark.edu
 
Abstract: The human papillomavirus (HPV) is the most common sexually transmitted virus in the U.S. To prevent multiple cancers attributable to the HPV, HPV vaccine is recommended for preteens and teens who have not been exposed to HPV. We develop a simulation model for the optimal design of personalized HPV vaccination program, which incorporates multiple social-behavioral and demographic risk factors. The efficacy of the HPV vaccination program is evaluated in terms of the HPV-related health outcomes.
  
Title: Tailoring CRC Screening Strategy for Different Age- and Gender-specific Population Subgroups
 Presenting Author: Carolina Vivas,cvivas@purdue.edu
 Co-Author: Thomas Imperiale,Indiana University, Department of Medicine, West Lafayette IN, United States of America,
 Robert Klein,Medical Decision Modeling, Inc., Indianapolis IN, United States of America,
 Nan Kong,Associate Professor, Purdue University, 206 S. Martin Jischke Dr., West Lafayette, United States of America, nkong@purdue.edu
 
Abstract: Standard guidelines for colorectal cancer (CRC) strategies do not consider different age- and gender-specific subgroups for tailored screening recommendations. Recent evidence suggests that men tend to face a higher risk of developing advance adenomas earlier than women. We apply Design of Experiments techniques to quantify the risk differences on CRC disease progression. Model based cost-effectiveness analyses of various screening strategies are conducted for different population subgroups.