AlcHepNet DCC UMMS

AlcHepNet DCC UMMS

• 批准号: 9980745
• 负责人: Bruce A Barton
• 金额: $ 85.19万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980745
• 关键词: Address; Alcoholic Hepatitis; Area; Bioinformatics; Biological Assay; Biological Specimen Banks; Biometry; Budgets; Clinical Trials; Common Data Element; Communication; Communication Tools; Communities; Companions; Complex; Data; Data Coordinating Center; Deposition; Development; Documentation; Ensure; Equipment and supply inventories; Funding; Health Sciences; Indiana; Infrastructure; Laboratories; Leadership; Liver; Massachusetts; Measures; Meta-Analysis; Methodology; Methods; Monitor; Morbidity - disease rate; National Institute on Alcohol Abuse and Alcoholism; Natural History; Outcome; Patients; Pharmaceutical Preparations; Phase; Pilot Projects; Process; Protocols documentation; Publishing; Quality Control; Randomized; Reporting; Reproducibility; Research; Research Design; Risk; Safety; Services; Site; Speed; Statistical Data Interpretation; Structure; System; Technology; Testing; Time; TimeLine; Treatment Efficacy; United States National Institutes of Health; Universities; base; biobank; data archive; data cleaning; data framework; data harmonization; data management; data portal; data warehouse; design; experience; innovation; medical schools; meetings; mortality; novel; operation; preclinical study; programs; quality assurance; response; symposium; tool; translational study

Abstract Alcoholic hepatitis (AH) is a leading cause of liver-related morbidity and mortality with a remarkable paucity of effective therapeutics. NIAAA has sponsored a number of RFAs to form the Alcoholic Hepatitis Network (AlcHepNet). Collectively, the network will synergize efforts and expertise to better understand AH and develop novel effective and safe therapies for severe AH. Because of the diversity of studies that will be proposed under these RFAs, two Data Coordinating Centers will be established to better support these studies efficiently and effectively to produce high quality reproducible research from each funded study. This application is for the Data Coordinating Center at the University of Massachusetts Medical School (DCC UMMS) for AlcHepNet which will provide the statistical and data management leadership to: (1) translational studies, (2) basic/pre-clinical studies, and (3) pilot clinical trials while using innovations based on previous experience to facilitate the operations of these studies with shorter timelines and greater quality. The DCC UMMS Team, housed in the Quantitative Methods Core (QMC) of the Department of Quantitative Health Sciences, is experienced in both NIH and FDA clinical trials and has implemented a number of proven, validated approaches in short timeframes and with minimal budgets with increased use of technology and monitoring tools. The Specific Aims of this proposal are: (1) Efficient and Effective Project Management - develop a robust infrastructure including communication support and administrative services to facilitate study objectives; (2) Rigorous Design and Analysis - provide high-level leadership in biostatistics and data management to the scientific methodology of the DCC UMMS supported studies; and (3) High Quality Study Implementation - ensure high quality of study results through a rigorous program of quality assurance and control, study monitoring, and DSMB and regulatory reporting. Each of these specific aims comprises a set of complex issues that determine efficiency and quality of implementation. Each will be addressed using multifaceted approaches that can address multiple issues at once. While there is a need for nimble response to changing needs across studies, there is also need for measured and documented approaches within the procedural/SOP framework of the DCC UMMS so that each approach is integrated within the DCC UMMS systems and contributes to higher quality, more reliable data and reproducible research.

摘要 酒精性肝炎(AH)是与肝脏相关的发病率和死亡率的主要原因，显著缺乏 有效的治疗方法。NIAAA赞助了一些RFA来组成酒精性肝炎网络 (AlcHepNet)。总体而言，该网络将协同努力和专业知识，以更好地了解AH并开发 治疗重症急性肝炎的新的有效和安全的疗法。因为将提出的研究的多样性 根据这些区域协调框架，将建立两个数据协调中心，以更有效地支持这些研究 并有效地从每项受资助的研究中产生高质量的可重复性研究。 此应用程序适用于马萨诸塞大学医学院(DCC)的数据协调中心 UMMS)，它将提供统计和数据管理领导，以：(1)翻译 研究，(2)基础/临床前研究，以及(3)在使用基于以前的创新的同时进行临床试验 经验，以更短的时间和更高的质量促进这些研究的运作。DCC UMMS团队，驻扎在定量卫生部门的定量方法核心(QMC) 在NIH和FDA的临床试验中都有经验，并实施了许多经过验证的、 在短时间内以最少的预算采用经过验证的方法，同时增加对技术和 监控工具。这项建议的具体目标是：(1)高效和有效的项目管理- 发展强大的基础设施，包括通信支持和行政服务，以促进学习 目标；(2)严谨的设计和分析-在生物统计和数据方面提供高水平的领导 管理DCC UMMS支持的研究的科学方法；以及(3)高质量的研究 实施-通过严格的质量保证和计划确保高质量的研究结果 控制，研究监测，以及dsmb和监管报告。 这些具体目标中的每一个都包括一系列复杂的问题，这些问题决定了工作的效率和质量 实施。每个问题都将使用多方面的方法来解决，这些方法可以解决多个问题 一次。虽然需要对不同研究中不断变化的需求做出灵活的反应，但也需要 在DCC UMMS的程序/SOP框架内衡量和记录的方法，以便每一种 该方法集成在DCC UMMS系统中，有助于提供更高质量、更可靠的数据和 可重复的研究。