Simulating Breast Cancer in Wisconsin

Principal Investigator: Dennis G. Fryback
Institution: University of Wisconsin - Madison
Grant Number: 1U01CA088211-01

Awarded under CA-99-013

Abstract: The purpose of the NCI Cancer Intervention and Surveillance Modeling Network (CISNET) is to promote simulation modeling as a tool that, in conjugation with the nation's cancer surveillance systems, can help to explain observed changes in cancer incidence and mortality. Wisconsin offers a unique population laboratory to develop and test breast cancer simulation models to meet this goal. We propose a collaboration among simulation and statistics experts, and surveillance and epidemiology experts at the University of Wisconsin, and the state of Wisconsin's Cancer Reporting System to study the use of simulation modeling to better understand trends in breast cancer epidemiology and to enhance the use of simulation modeling for this purpose.

This project will update and extend a previously developed model simulating breast cancer age- and stage-specific incidence and age-specific mortality in Wisconsin. The model was developed and validated in 1992-93 and was used to explain breast cancer trends in Wisconsin from 1982-1992. We will reprogram the macrosimulation model as a discrete event microsimulation, updating inputs to account for demographic, and breast cancer detection and treatment changes since 1992. Modifications will also incorporate new epidemiologic knowledge about environmental, behavioral, and genetic risk factors, with special attention to account for new knowledge about breast cancer in situ. In making these changes we will explore use of Bayesian estimation techniques for fitting simulation parameters in light of time-based surveillance data. The model will be placed on a geographic information analysis platform along with geo-coded surveillance data and devise techniques for systematic comparison of regional variations between model predictions and surveillance data. Collaboration with other CISNET grantees will explore issues in model development, validation, and use.