Delaware Clinical and Translational Research ACCEL Program (BERD Core)

抽象-BERD 特拉华州Accel CTR的生物统计学、流行病学和研究设计(BERD)核心已经成熟 在过去的9年中，成为一个庞大、稳定但充满活力的核心，拥有一系列技能和资源，能够 高质量的临床和翻译(C&T)研究。贝尔德·S的成就源于密切的合作 Accel伙伴机构之间的合作：特拉华大学、内穆尔儿童健康系统、 Christian Care Health Services Inc.、南卡罗来纳医科大学和特拉华州立大学。 BERD汇集了由熟练的生物统计学家、流行病学家和生物信息学家组成的不同核心 具有促进翻译科学发展和协助C&T调查人员的共同目标的机构。这个 来自MUSC的BERD团队帮助我们启动和开发了强大、可持续的BERD 在过去的九年里，但基于DE的BERD团队已经成熟到了独立的程度。第3阶段BERD (没有MUSC)将在这一势头的基础上满足C&T研究所需的不断发展的技能。与 随着海量数据可用性的不断提高，我们有可能更深入地了解 疾病在个人和群体层面的决定因素。要做到这一点，我们必须开发新的方法 以及分析这些数据的工具。我们将通过机器学习(ML)方面的专业知识来丰富BERD资源， 自然语言处理(NLP)、云计算、地理空间分析和新的生物统计方法 解决健康差距问题。我们提出了以下具体目标：1)扩大和丰富咨询服务 用于临床、转化、人口健康科学。BERD将利用ML、NLP、云 一些Accel-CTR机构中已经存在的计算和地理空间分析，并将使 向所有研究人员提供专业知识；2)开发生物统计和数据科学方面的创新方法 包括减少选择偏差，增强研究的力量，以及开发设计和分析 计划对脆弱人群或极少数受试者进行研究；3)促进虚拟自我调整 同步和异步的学习机会。我们将继续提供TechTalk和创新 发现系列讲座并将审查在线材料以发布在我们的教育中心；4)扩展 利用过去九年获得的专业知识和技能，建立生物医学信息学研究基础设施 建立一个整合的资源网络，专注于解决特拉华州的关键医疗需求。 BERD的具体目标建立在Accel CTR阶段3的总体目标基础上。通过扩展BERD 为了更好地满足调查人员的专业知识需求，我们将完善科学基础设施和文化。我们 将制定创新的方法，以促进旨在改善卫生公平的项目。访问云 将允许临床医生和科学家团队开发具有高度临床影响的项目。进入 异步学习将帮助临床研究人员提高他们在生物统计学/流行病学方面的技能。

ABSTRACT - BERD The Biostatistics, Epidemiology and Research Design (BERD) Core of the Delaware ACCEL CTR has matured in the last 9 years into a large, stable yet dynamic core with a repertoire of skills and resources that enables quality clinical and translational (C&T) research. BERD’ s accomplishments result from the close collaboration between ACCEL partner institutions: the University of Delaware, the Nemours Children’ Health system, ChristianaCare Health Services Inc., the Medical University of South Carolina and Delaware State University. BERD assembles a diverse core of skilled biostatisticians, epidemiologists, and bioinformaticians from each Institution who have the common goal of advancing translational science and assist C&T investigators. The BERD team from MUSC was instrumental in helping us launch and develop a strong, sustainable BERD over the last nine years, but the DE-based BERD team has matured to a point of independence. Phase 3 BERD (without MUSC) will build on this momentum to meet the evolving skills needed for C&T research. With the increasing availability of vast amounts of data, we have the potential to provide deeper insight into the determinants of disease at the individual and population level. To do so, we must develop new methodologies and tools to analyze these data. We will enrich the BERD resources with expertise in Machine Learning (ML), Natural language Processing (NLP), cloud computing, geospatial analysis and novel biostatistics methods to address health disparities. We propose the following specific aims:1) Expand and enrich consultative services for clinical, translational, population health sciences. BERD will leverage the expertise in ML, NLP, cloud computing, and geospatial analysis that already exists in some ACCEL-CTR Institutions and will make this expertise available to all Investigators; 2) Develop innovative methods in biostatistics and data sciences including reducing selections biases, enhancing the power of studies, and developing design and analysis plans for studies of vulnerable populations or very small numbers of subjects; 3) Promote virtual self-paced synchronous and asynchronous learning opportunities. We will continue to offer TechTalks and Innovative Discoveries Series lectures and will vet on-line material for posting on our Education hub; 4) Expand the biomedical informatics research infrastructure by leveraging expertise and skills acquired in the last nine years to establish an integrated network of resources focused on addressing critical healthcare needs in Delaware. The BERD specific aims build upon the overall aims of the ACCEL CTR phase 3. By expanding BERD expertise to better meet the needs of Investigators, we will improve the scientific infrastructure and culture. We will develop Innovative methodologies to foster projects aiming at improving health equity. Access to the cloud will allow teams of clinicians and scientists to develop projects that have high clinical impact. Access to asynchronous learning will help clinician researchers to sharpen their skills in biostatistics/epidemiology.