Analytics, trial methods and modeling

对精准医疗的一个常见批评是，它可能会为个人提供治疗益处，但却不能为个人提供治疗益处。 解决更广泛的人口健康差距;精准医疗甚至可能加剧健康差距， 弱势群体对新型筛查和治疗的利用率低得不成比例 1，2为了回应这一批评，分析和建模（A&M）核心的职责是建立桥梁， 个人层面的精准医疗和人口层面的健康差异之间的鸿沟， 斯坦福大学的主要优势：（一）在个人层面上使用组学数据的专业知识， 评估疾病筛查和治疗策略，以及（ii）计算机建模方法的专业知识 统称为“系统科学”，它整合了 来自个体的数据（组学数据，临床数据， 行为调查数据）以及关于关键背景因素的数据（获得医疗保健的社会和经济障碍， 文化因素、环境因素、与健康有关的政策），以研究 人口 利用斯坦福大学在这些领域的独特专长，A&M核心将追求以下目标 目的：（1）整合来自美洲土著关节炎和多种族乳腺癌R 01项目的数据 到特定问题的系统科学模型，以执行杠杆点分析，其中涉及整合 个体水平的组学数据与临床，生物学和背景数据，以确定哪些精准医学 干预措施最适合改善个体患者的结局、人群水平的健康差异或两者兼而有之; 5，6 (2)通过研究拉丁美洲肥胖R 01项目的成本效益， 将个性化组学分析（iPOP）整合到多组分，多环境干预中，以减少 拉丁裔青年的体重增加;（3）作为创新数据部署的卓越中心 整合、分析和建模策略，弥合精准医学和人口之间的鸿沟 健康我们的模型将整合每个研究人群中的多层次数据，社会风险变化， 以及影响筛查和治疗、筛查表现、患者对测试的反应的文化障碍 结果，从筛选结果到治疗功效的临床增益，患者对建议疗法的反应， 和治疗的效果通过将这些不同的数据整合到模拟模型中， 个体水平的因素对疾病风险和结果的人群差异的影响，我们可以确定 改变患者结果和人口差异的关键组成部分-杠杆， 未来精准医疗计划的目标和发展。使用系统科学技术，A&M 核心将创建，验证和实施模型，确定如何筛选和治疗基于组学 数据可以最有效和最具成本效益地减少弱势群体之间的差异风险， 最终引领精准医学干预策略的兴趣，这将减少人口差异， 疾病风险和治疗结果。

A common criticism of precision medicine is that it may offer treatment benefits to individuals, but fail to address broader population disparities in health; precision medicine may even exacerbate health disparities if disadvantaged populations have disproportionately low utilization of novel screening and treatment technologies.1,2 In response to this criticism, the charge of the Analytics and Modeling (A&M) Core is to bridge the divide between individual-level precision medicine and population-level health disparities by leveraging two major strengths at Stanford University: (i) expertise in using omics data at the individual level to design and evaluate disease screening and treatment strategies, and (ii) expertise in computer modeling methods collectively referred to as “systems science”, which integrate data from individuals (omics data, clinical data, behavioral survey data) with data on key contextual factors (social and economic barriers to healthcare access, cultural factors, environmental factors, health-related policies) to study the distribution of health in the population. Leveraging Stanford's unique expertise in these areas, the A&M core will pursue the following objectives: (1) to integrate data from the Native American arthritis and multi-ethnic breast cancer R01 projects into problem–specific systems science models to perform leverage point analysis, which involves integrating individual-level omics data with clinical, biological, and contextual data to identify which precision medicine interventions are best for improving individual patient outcomes, population-level health disparities, or both;5,6 (2) conduct cost-effectiveness analyses for the Latino obesity R01 project, by studying the cost-effectiveness of integrating personalized –omics profiling (iPOP) into a multi-component, multi-setting intervention to reduce weight gain among Latino youth; and (3) serve as a center for excellence for the deployment of innovative data integration, analysis and modeling strategies to bridge the divide between precision medicine and population health. Our models will integrate multi-level data on omic risk variations within each studied population, social and cultural barriers that influence screening and treatment, screening performance, patient responses to test results, clinical gains to therapeutic efficacy from screening results, patient responses to suggested therapies, and patient outcomes from therapy. By integrating these diverse data into simulation models that link these individual-level factors to population disparities in disease risk and outcomes, we can identify the impact of altering key components – the levers – of patient outcomes and of population disparities, to inform the targeting and development of future precision medicine programs. Use systems science techniques, the A&M Core will create, validate, and implement models that identify how screening and treatment based on omics data can most effectively and cost-effectively reduce differential risk among vulnerable populations, in the interest of eventually leading precision medicine intervention strategies that will reduce population disparities in disease risk and treatment outcomes.