NORTH CAROLINA STATE UNIVERSITY
EDWARD P. FITTS DEPARTMENT
OF INDUSTRIAL AND SYSTEMS ENGINEERING
RESEARCH SEMINAR
ISE 601/801
Friday November 13, 2015
434 Daniels Hall
11:30 am
Natural History Models: Data, Models, and Decisions …
And Unintended Consequences
Dr. Julie Higle
Abstract
Cancer screening strategies facilitate early detection of cancer in a systematic
fashion. Early detection can lead to improved treatment prospects, increased
survival rates, improved quality of life for survivors, and reduced treatment costs.
Increasingly often, model-based analyses of screening and treatment strategies are
used to inform health policy and its implementation. They permit an exploration of
a broader range of strategies than might be tested in clinical studies, including
those that are configured hypothetically.
A central component of a model-based analysis is the natural history model, which
represents the evolution of disease in the absence of medical intervention. The
development of a natural history model is an intricate process, requiring significant
navigation around issues involving “data” and “model parameters”. The
construction of the model involves various data sources, and modeling techniques
are necessary to estimate data that are not available through clinical studies.
This seminar will discuss experiences with natural history models for cervical and
ovarian cancers, and will segue into an exploration of the way that our results and
recommendations are impacted by the way that we explore our models.
A healthy discussion of the pros and cons of various modeling decisions will
undoubtedly be included.
Refreshments will be served in Daniels Hall room 428
Student Lounge from 11:00 a.m. to 11:30 a.m.
Dr. Julie Higle
Professor and Chair, Industrial and Systems Engineering
University of Southern California
Biography
Julie Higle serves as Professor and Chair of the Daniel J. Epstein Department of
Industrial and Systems Engineering at the University of Southern California. Prior
to joining USC, she has served as a member of the faculty of Systems and
Industrial Engineering at the University of Arizona, and as the chair of the
Department of Integrated Systems Engineering at The Ohio State University. Her
research interests are primarily in the development of models and solutions
methods for decision making under uncertainty, with a healthy emphasis on
medical decision making.