Analytics, trial methods and modeling

对精准医疗的一种常见批评是，它可能会为个人提供治疗益处，但未能做到 解决更广泛的人群健康差距；精准医学甚至可能加剧健康差距，如果 弱势群体对新的筛查和治疗的利用率极低 技术。1，2作为对这种批评的回应，分析和建模(A&M)核心的职责是在 个人水平的精准医学和人口水平的健康差距之间的差距通过利用两个 斯坦福大学的主要优势：(I)在个人层面使用组学数据设计和 评估疾病筛查和治疗策略，以及(2)计算机建模方法方面的专业知识 统称为“系统科学”，它把 来自个人的数据(组学数据、临床数据、 行为调查数据)和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、健康相关政策)，以研究健康在中国的分布 人口。 利用斯坦福在这些领域的独特专业知识，并购核心将追求以下几点 目标：(1)整合美洲原住民关节炎和多种族乳腺癌R01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，这涉及集成 个人层面的组学数据与临床、生物学和背景数据相结合，以确定哪些精准医学 干预措施最有利于改善个别患者的结局和/或人群水平的健康差距；5，6 (2)对拉美裔肥胖R01项目进行成本效益分析，通过成本效益研究 将个性化组学分析(IPOP)集成到多组件、多设置干预中，以减少 在拉美裔青年中体重增加；以及(3)作为部署创新数据的卓越中心 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。我们的模型将整合关于每个被研究人群、社会经济风险变化的多层次数据 以及影响筛查和治疗、筛查性能、患者对检测的反应的文化障碍 结果，从筛查结果中获得的临床疗效，患者对建议治疗的反应， 以及患者接受治疗后的结果。通过将这些不同的数据集成到模拟模型中，将这些 个体水平的因素对人群疾病风险和结局的差异，我们可以确定其影响 改变患者结局和人群差异的关键组成部分-杠杆-以告知 未来精准医学计划的目标和发展。运用系统科学技术，并购 CORE将创建、验证和实施模型，以确定基于组学的筛查和治疗 数据可以最有效、最具成本效益地降低脆弱人群之间的差异风险，在 有兴趣最终领导精准医学干预战略，以缩小人口差距 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.