Longevity, Equity, and Aging Research Network (L.E.A.R.N.) Consortium Analysis Core

项目摘要-分析核心 长寿，公平和老龄化研究网络的分析核心（AnC）的总体目标 （L.E.A.R.N）联盟将提供生物统计学、流行病学和方法学培训、指导和 援助，并指导来自不同和代表性不足的群体（统称为初级调查员） RCMAR科学家）进行跨学科民族老年医学健康差异研究， 综合了生物学、社会学、行为学和文化学的考虑。我们将训练他们使用各种 创新技术，包括新出现的方法（即，生物医学数据科学，数字健康方法， 精确医学技术和虚拟现实和基于模拟的干预措施），定性研究， 混合方法研究和实施科学。AnC将审查、监测和提供指导 并继续为我们所有的RCMAR科学家提供生物统计学，生物信息学和数据库支持。我们会密切 在我们的中心与领导和管理核心和研究教育合作 为RCMAR科学家提供持续的个性化咨询，培训和教育。我们 还将与L.E.A.R.N联盟教师、RCMAR协调 中心和我们的姐妹RCMAR中心在全国各地。我们核心的力量在于我们经验丰富的核心 调查人员具备互补的专门知识/经验，并特别致力于指导和教育 早期的职业研究者。沿着指导和咨询，AnC将提供正规教育 通过斯坦福大学精神病学系的统计学和方法学课程。Anc和研究 教育部分将密切合作，指导RCMAR科学家根据 他们的试点项目和个性化的培训计划的需求。AnC还将协助RCMAR科学家 为试点项目提供安全和高质量的数据库管理。正如RCMAR科学家们早期 职业研究人员，将特别注意确保，无论试点项目的性质，他们将 在处理人类受试者数据时要非常谨慎，这也将提供一个关键的 为他们提供了与专家一起培训和学习的机会，以正确管理人类受试者数据。在 除了支持RCMAR科学家，AnC还将积极发展尖端统计和研究 方法的共同目标是促进护理和研究来自不同背景的老年人。 利用在拟议的RCMAR下自然形成的跨学科论坛 计划，AnC将领导和鼓励项目调查人员在各种统计和 与民族老年学有关的方法学研究课题，如生物医学数据科学方法， 生物信息学、数字健康、虚拟现实和仿真研究设计、个性化（精确）医学、 定性和混合方法，比较有效性，行为干预，效果异质性， 联合利用多种分析传统，有效处理大量信息。

PROJECT SUMMARY—ANALYSIS CORE The overall goal of the Analyses Core (AnC) of the of the Longevity, Equity, and Aging Research Network (L.E.A.R.N) Consortium is to provide biostatistical, epidemiological, and methodological training, mentoring and assistance, and to guide junior investigators from diverse and underrepresented groups (collectively called RCMAR Scientists) in the conduct of transdisciplinary ethnogeriatrics health disparities research that integrates biological, social, behavioral, and cultural considerations. We will train them to use various innovative techniques, including emerging methodologies (i.e., biomedical data science, digital health methods, precision medicine techniques and virtual reality and simulation-based interventions), qualitative research, mixed methods research, and implementation science. The AnC will review, monitor, and provide guidance and continuing biostatistical, bioinformatics and database support for all our RCMAR Scientists. We will closely collaborate within our center with the Leadership and Administration Core and the Research Education Component to provide continuing individualized consulting, training, and education for RCMAR Scientists. We will also collaborate and share resources with the L.E.A.R.N Consortium faculty, the RCMAR Coordinating Center and our sister RCMAR centers across the nation. The strength of our core lies in our experienced core investigators with complementary expertise/experience and exceptional devotion to mentoring and education of early career researchers. Along with mentoring and consulting, the AnC will provide formal education through statistics and methods classes in the Stanford Psychiatry Department. The AnC and the Research Education Component will work closely to direct RCMAR Scientists to the appropriate courses based on the needs of their pilot projects and their personalized training plan. The AnC will also assist the RCMAR Scientists with safe and high-quality database management for the pilot projects. As the RCMAR Scientists are early career researchers, special care will be taken to ensure that, regardless of the nature of pilot projects, they will be mentored to exercise great caution in handling the human subject data, which will also provide a crucial opportunity for them to train with and learn from experts on proper management of human subject data. In addition to supporting RCMAR Scientists, the AnC will actively develop cutting edge statistical and research methods with the common goal of advancing care of and research on older adults from diverse backgrounds. Taking advantage of the interdisciplinary forum that will be naturally formed under the proposed RCMAR program, the AnC will lead and encourage project investigators to collaborate on various statistical and methodological research topics pertinent to ethnogeriatrics, such as biomedical data science approaches, bioinformatics, digital health, virtual reality and simulation study design, personalized (precision) medicine, qualitative and mixed methods, comparative effectiveness, behavioral interventions, effect heterogeneity, and joint utilization of multiple analytical traditions to effectively handle multitude of information.