MECHANISTIC MODELING OF BREAST CANCER SURVEILLANCE

乳腺癌监测的机制建模

• 批准号: 6684191
• 负责人: ANDREI Y YAKOVLEV
• 金额: $ 12.6万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6684191
• 关键词: breast neoplasm /cancer diagnosis; breast neoplasms; clinical research; cooperative study; health care model; human data; mass screening; mathematical model; model design /development; neoplasm /cancer epidemiology; women's health

The general objective of this project is to consider the utility of mechanistic models of tumor development and detection in analysis of the impact of breast cancer screening in population- based settings. A stochastic model of cancer screening we propose offers the following distinct advantages: 1. It provides a simple but still realistic description of cancer latency; 2. It can be generalized in various ways while retaining its basic structure; 3. It furnishes a biologically meaningful interpretation of data analyses; 4. It accommodates standard population-based statistical data; its implementation does not depend heavily on availability of the data yielded by screening trials; 5. Rigorous statistical methods are available for estimating model parameters; 6. It can be used for designing optimal strategies of cancer screening and surveillance. The model will be validated with data on breast cancer from the Utah Population Data Base and the Utah Cancer Registry. Using these resources we will obtain initial parameter values for a pertinent estimation algorithm designed for grouped data on breast cancer mortality provided by the National Center for Health Statistics. This two-step estimation procedure will be tested by computer simulations and analyses of epidemiological data. In addition, we will explore the utility of stochastic approximation techniques in estimation of model parameters within the microsimulation framework.

本项目的总体目标是考虑肿瘤发展和检测的机制模型在分析基于人群的乳腺癌筛查的影响中的效用。 我们提出的癌症筛查的随机模型提供了以下明显的优点：1。它提供了一个简单但仍然现实的癌症潜伏期的描述; 2.它可以在保持其基本结构的情况下以各种方式进行推广; 3.它解释了数据分析的生物学意义; 4.它容纳了标准的基于人口的统计数据;它的实施并不严重依赖于筛选试验产生的数据的可用性; 5.严格的统计方法可用于估计模型参数; 6.它可用于设计癌症筛查和监测的最佳策略。该模型将使用来自犹他州人口数据库和犹他州癌症登记处的乳腺癌数据进行验证。 使用这些资源，我们将获得初始参数值的相关估计算法设计的分组数据乳腺癌死亡率由国家卫生统计中心提供。 这两步估计程序将通过计算机模拟和流行病学数据分析进行测试。 此外，我们将探讨在微观模拟框架内的模型参数估计的随机近似技术的效用。