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

项目总结-分析核心 长寿、股票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 协助，并指导来自不同和代表性不足群体的初级调查人员(统称为 RCMAR科学家)进行了跨学科的人种老年学健康差异研究 综合了生物、社会、行为和文化方面的考虑。我们将培训他们使用各种不同的 创新技术，包括新兴方法学(即生物医学数据科学、数字保健方法、 精确医学技术、虚拟现实和基于模拟的干预)、定性研究、 混合方法研究和实施科学。非国大将审查、监督和提供指导 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。我们将密切合作 在我们的中心内与领导力和管理核心以及研究教育合作 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联盟教职员工、RCMAR协调中心合作并共享资源 中心和我们的姐妹RCMAR中心遍布全国。我们核心的力量在于我们经验丰富的核心 具有互补专业知识/经验并特别致力于指导和教育的调查人员 早期职业研究人员。除了辅导和咨询，非国大还将提供正规教育 通过斯坦福精神病学系的统计和方法课程。非国大与研究 教育部门将密切合作，指导RCMAR科学家根据 他们的试点项目和个性化培训计划的需求。非国大还将协助RCMAR的科学家 以安全、优质的数据库管理为试点项目。因为RCMAR的科学家们很早 对于职业研究人员，将特别注意确保，无论试点项目的性质如何，他们都将 指导在处理人类主题数据时非常谨慎，这也将提供关键的 为他们提供了与专家一起培训和学习适当管理人类主题数据的机会。在……里面 除了支持RCMAR科学家外，非国大还将积极发展尖端统计和研究 方法以促进对来自不同背景的老年人的护理和研究为共同目标。 利用将在拟议的区域协调机制下自然形成的跨学科论坛 计划，非国大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主题，如生物医学数据科学方法， 生物信息学、数字健康、虚拟现实和模拟研究设计、个性化(精确)医学、 定性和混合方法、比较有效性、行为干预、效果异质性以及 联合利用多种分析传统，有效地处理大量信息。

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.