SIMULATING BREAST CANCER IN WISCONSIN

NCI癌症干预和监测建模网络（CISNET）的目的是促进模拟建模作为一种工具，与国家的癌症监测系统相结合，可以帮助解释观察到的癌症发病率和死亡率的变化。 威斯康星州提供了一个独特的人口实验室，以开发和测试乳腺癌模拟模型，以满足这一目标。 我们建议在模拟和统计专家，监测和流行病学专家在威斯康星州的大学，和威斯康星州的癌症报告系统的状态之间的合作，研究使用模拟建模，以更好地了解乳腺癌流行病学的趋势，并提高使用模拟建模为此目的。该项目将更新和扩展先前开发的模型，模拟威斯康星州的乳腺癌年龄和阶段特异性发病率和年龄特异性死亡率。 该模型于1992-93年开发和验证，并用于解释1982-1992年威斯康星州的乳腺癌趋势。 我们将重新编程的宏观模拟模型作为一个离散事件微观模拟，更新输入占人口，乳腺癌检测和治疗的变化，自1992年以来。 修改还将纳入有关环境，行为和遗传风险因素的新流行病学知识，特别注意考虑有关原位乳腺癌的新知识。 在进行这些改变时，我们将探索使用贝叶斯估计技术，根据基于时间的监测数据拟合模拟参数。 该模型将与地理编码监测数据一起沿着放在地理信息分析平台上，并设计技术，对模型预测和监测数据之间的区域差异进行系统比较。 与其他CISNET受让人的合作将探讨模型开发，验证和使用中的问题。

The purpose of the NCI Cancer Intervention and Surveillance Modeling Network (CISNET) is to promote simulation modeling as a tool that, in conjunction 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.