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Applications of Multi-Criteria Optimization (AMCO) to Cancer Simulation Modeling

多标准优化 (AMCO) 在癌症模拟建模中的应用

基本信息

批准号：
7788024
负责人：
Chung Yin Kong
金额：
$ 16.38万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-24 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7788024
关键词：
Address Algorithms Arts Award Calibration Cancer Biology Cancer Model Clinical Data Clinical Trials Colorectal Cancer Communities Complex Computer Simulation Computer software Computers Data Decision Analysis Development Disease model Doctor of Philosophy Engineering Enrollment Ethics Event General Hospitals Goals Growth Guidelines Health Policy Healthcare Hybrids Institutes Institution Internet Intervention K-Series Research Career Programs Language Life Malignant Neoplasms Malignant neoplasm of lung Massachusetts Measures Mentors Mentorship Methodology Methods Modeling Natural History Neoplasm Metastasis Outcome Outcomes Research Performance Physics Policies Policy Maker Polymers Probability Procedures Process Public Health Randomized Clinical Trials Randomized Controlled Clinical Trials Research Research Personnel Research Training Sampling Science Screening for cancer Senior Scientist Space Models Speed Study models Techniques Technology Assessment Testing Time Training Yin career career development density design design and construction experience follow-up improved instructor medical schools model development models and simulation mortality physical science programs public health relevance simulation skills symposium tool tumor growth tumor registry

项目摘要

DESCRIPTION (provided by applicant): Cancer screening programs are increasingly evaluated with simulation models because they allow health policy makers to consider scenarios that could not be evaluated by randomized clinical trials for practical, financial or ethical reasons. However, few of these models employ rigorous mathematical methods for model calibration. Calibration of cancer screening simulation models to existing clinical data is vital to accurate model prediction. The applicant's immediate goal is to adapt, extend, and promote the use of multi-criteria optimization techniques to improve the calibration of simulation models for cancer screening policy prediction and planning. The applicant, Chung Yin Kong, PhD, is a senior scientist at the Massachusetts General Hospital's Institute for Technology Assessment (ITA) and an instructor at Harvard Medical School. He is trained in Physics (BS) and Polymer Science and Engineering (PhD). This proposed research is tailored to utilize his computer modeling background in physical science as well as the numerous simulation projects at the ITA to test his hypotheses for improving the design and construction of cancer screening models with multi-criteria optimization techniques. The specific aims of the research plan are: (1) to adapt multi-criteria optimization to provide automated procedures for model calibration. As an example, optimization algorithms will be applied to and evaluated with two existing microsimulation models at the ITA: the Lung Cancer Policy Model (LCPM) and the Simulation Model of Colorectal Cancer (SimCRC) model; (2) to extend the use of multi-criteria optimization techniques to aid the design of the underlying cancer biology components in the models and to improve computational speed; (3) to promote the use of multi-criteria optimization techniques among cancer screening modelers. The experience of adapting and extending these techniques will be developed into a calibration platform with instructional diagrams, tutorials, and software modules, which will be distributed on the Internet and at scientific conferences. The end results of the proposed project will improve the speed of both the calibration process and the simulation models themselves. The proposed training plan includes mentoring, coursework, and career development activities preparing him to undertake the proposed research and to fully-transition into the field of cancer simulation modeling. The research and training of this proposed project will be performed under the mentorship of Dr. G. Scott Gazelle, an internationally known expert in cancer outcome research and decision analysis science. The applicant's long term career goal is to become a leader in developing state-of-the-art simulation methods for disease modeling. This award will advance the applicant's academic career and help him to achieve his goal to be a productive, independent investigator. PUBLIC HEALTH RELEVANCE: This research is relevant to public health because it improves the accuracy of simulation models for cancer screening policy prediction and planning.

癌症筛查计划越来越多地使用模拟模型进行评估，因为它们允许卫生政策制定者考虑由于实际，财务或道德原因而无法通过随机临床试验进行评估的情况。然而，这些模型很少采用严格的数学方法进行模型校准。根据现有临床数据校准癌症筛查模拟模型对于准确的模型预测至关重要。申请人的近期目标是适应，扩展和促进多标准优化技术的使用，以改善癌症筛查政策预测和规划的模拟模型的校准。申请人Chung Yin Kong博士是马萨诸塞州总医院技术评估研究所（ITA）的资深科学家，也是哈佛医学院的讲师。他接受过物理学（BS）和聚合物科学与工程（PhD）的培训。这项拟议的研究是专门利用他在物理科学的计算机建模背景，以及在ITA的众多模拟项目，以测试他的假设，以改善设计和建设的癌症筛查模型与多标准优化技术。研究计划的具体目标是：（1）采用多标准优化，为模型校准提供自动化程序。举例来说，优化算法将应用于ITA现有的两个微观模拟模型：肺癌政策模型（LCPM）和结直肠癌模拟模型（SimCRC），并对其进行评估;（2）扩展多标准优化技术的使用，以帮助设计模型中的基本癌症生物学组件，并提高计算速度;（3）在癌症筛查模型制作者中推广多准则优化技术的使用。调整和扩展这些技术的经验将被开发成一个校准平台，包括教学图表、教程和软件模块，并将在互联网和科学会议上分发。拟议项目的最终结果将提高校准过程和仿真模型本身的速度。拟议的培训计划包括指导，课程和职业发展活动，使他能够进行拟议的研究并完全过渡到癌症模拟建模领域。本项目的研究和培训将在G。Scott Gazelle是国际知名的癌症结果研究和决策分析科学专家。申请人的长期职业目标是成为开发最先进的疾病建模模拟方法的领导者。该奖项将促进申请人的学术生涯，并帮助他实现成为一名富有成效的独立调查员的目标。公共卫生相关性：这项研究与公共卫生有关，因为它提高了癌症筛查政策预测和规划模拟模型的准确性。