MECHANISTIC MODELING OF BREAST CANCER SURVEILLANCE

乳腺癌监测的机制建模

• 批准号: 6378137
• 负责人: ANDREI Y YAKOVLEV
• 金额: $ 13.23万
• 依托单位: UNIVERSITY OF UTAH
• 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/6378137
• 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.

这个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以人群为基础的乳腺癌筛查的影响方面的实用性。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.它提供了对癌症潜伏期的简单但仍然真实的描述；2.它可以以各种方式推广，同时保持其基本结构；3.它为数据分析提供了具有生物学意义的解释；4.它容纳了标准的基于总体的统计数据；它的实施不严重依赖于筛查试验所产生的数据的可用性；5.可用严格的统计学方法来估计模型参数；6.它可用于设计癌症筛查和监测的最优策略。该模型将用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，我们将获得由国家卫生统计中心提供的针对乳腺癌死亡率分组数据设计的相关估计算法的初始参数值。这一两步评估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，我们还将在微观模拟框架内探索随机逼近技术在模型参数估计中的应用。