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

描述（由申请人提供）：癌症筛查项目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标定。将癌症筛查模拟模型与现有临床数据进行校准对于准确预测模型至关重要。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Kong Chung Yin，博士，麻省总医院技术评估研究所（ITA）资深科学家，哈佛大学医学院讲师。他接受过物理学（学士学位）和聚合物科学与工程（博士学位）的培训。本研究旨在利用他在物理科学方面的计算机建模背景以及ITA的众多模拟项目来验证他的假设，以改进采用多标准优化技术的癌症筛查模型的设计和构建。研究计划的具体目标是：(1)适应多准则优化，为模型校准提供自动化程序。作为实例，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）模型并进行评估；(2)扩展多准则优化技术的使用，以帮助设计模型中潜在的癌症生物学组件并提高计算速度；(3)促进多标准优化技术在癌症筛查建模者中的应用。调整和扩展这些技术的经验将发展成为一个带有指导性图表、教程和软件模块的校准平台，并将在互联网和科学会议上分发。该计划的最终结果将提高校准过程和模拟模型本身的速度。拟议的培训计划包括指导、课程和职业发展活动，为他承担拟议的研究和完全过渡到癌症模拟建模领域做好准备。本项目的研究和培训将在国际知名癌症结局研究和决策分析科学专家G. Scott Gazelle博士的指导下进行。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，并帮助他实现他的目标，成为一个富有成效的，独立的研究者。

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.