Comparative Modeling of Effective Policies for Colorectal Cancer Control

摘要 结直肠癌(CRC)是美国癌症死亡的第二大原因。长期目标 我们建议的项目的目的是通过提供所需的信息来减轻儿童权利中心的人口负担 以可访问和透明的方式解决整个CRC控制过程中的关键政策问题。至 为了实现这一目标，我们将使用我们的基于人口的微模拟模型来：1)评估 按照美国的做法进行筛查；2)告知有关50岁前结直肠癌发病率增加的辩论；3) 考虑筛查和监测的精确度的有效性；4)解决其他新出现的问题和 在CRC控制方面的机会；以及5)使用新的方法来提高模型的可及性和透明度。我们的 团队将填补知识的关键空白，使决策者能够采取行动。我们将纳入新的证据 在我们的模型中，为了更好地告知CRC控制机会，将1)更新有关筛查模式的信息 美国(与基于人口的研究机构合作)通过个性化优化筛查 方案，或PROSPR)，2)关于50岁以下人群患结直肠癌风险增加的数据(与 美国癌症协会的丽贝卡·西格尔，她在这一领域做出了开创性的工作)，以及最先进的 结肠镜筛查数据将不同的致癌途径纳入自然病史模型 (与新罕布夏州结肠镜检查登记处合作)。我们将综合和整合 评估个性化筛查和治疗的临床效用的文献证据越来越多， 以及新型计算机辅助检测和诊断模式的潜在作用。我们将扩大我们的 为正在考虑 实施筛查计划。最后，迫切需要使我们的模型可评估和 透明的。为此，我们将使用高性能计算方法来开发和应用深度- 模型校准和模型仿真的学习方法，这将有助于模型共享。三位一体 参赛的模特团体很好地开展了这项工作，带来了丰富的经验， 具有与CRC微观模拟建模相关的专业知识和洞察力，并有良好的记录 合作并将我们的工作传播给卫生政策决策者。

ABSTRACT Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The long-term goal of our proposed project is to reduce the population burden of CRC by providing the information needed to address key policy questions across the CRC control continuum in an accessible and transparent manner. To accomplish this goal we will use our population-based microsimulation models to: 1) Evaluate the impact of screening as practiced in the US; 2) Inform the debate about the increase in CRC incidence before age 50; 3) Consider the effectiveness of precision of screening and surveillance; 4) Address other emerging issues and opportunities in CRC control; and 5) Use novel methods to improve model accessibility and transparency. Our team will fill critical gaps in knowledge, enabling decision makers to act. New evidence that we will incorporate in our models to better inform CRC control opportunities will be 1) updated information on screening patterns in the US (in collaboration with the Population-based Research Optimizing Screening through Personalized Regimen, or PROSPR), 2) data on the increased risk of CRC in persons under age 50 (in collaboration with Rebecca Siegal of the American Cancer Society, who did the seminal work in this area), and 3) state-of-the art colonoscopy screening data to incorporate alternative carcinogenesis pathways in the natural history models (in collaboration with the New Hampshire Colonoscopy Registry). We will synthesize and incorporate the growing body of evidence in the literature to assess the clinical utility of personalized screening and treatment, as well as the potential role for novel computer-aided detection and diagnosis modalities. We will expand our models to project clinical and resource-based outcomes for middle-income countries that are considering the implementation of a screening program. Lastly, there is a critical need to make our models assessible and transparent. To this end we will use high performance computing approaches to develop and apply deep- learning methods for model calibration and model emulation, which will aid in model sharing. The three participating modeling groups are well positioned to carry out this work, bringing a wealth of experience, expertise, and insight to issues related to microsimulation modeling of CRC, and have a proven track record of collaboration and disseminating our work to health policy decision makers.