AlcHepNet DCC UMMS

AlcHepNet DCC UMMS

• 批准号: 10428563
• 负责人: Bruce A Barton
• 金额: $ 82.59万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10428563
• 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; United States National Institutes of Health; Universities; base; biobank; data archive; data cleaning; data framework; data harmonization; data management; data portal; data repository; design; experience; implementation facilitation; innovation; medical schools; meetings; mortality; novel; operation; preclinical study; programs; quality assurance; response; symposium; therapeutically effective; 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.

抽象的 酒精性肝炎 (AH) 是肝脏相关发病率和死亡率的主要原因，但其发病率却非常低 有效的治疗方法。 NIAAA 赞助了许多 RFA 来组建酒精性肝炎网络 （AlcHepNet）。总的来说，该网络将协同努力和专业知识，以更好地了解 AH 并开发 针对严重 AH 的新型有效且安全的疗法。由于将提出的研究具有多样性 根据这些 RFA，将建立两个数据协调中心，以更好地有效支持这些研究 并有效地从每项资助的研究中产生高质量的可重复的研究。 此应用程序适用于马萨诸塞大学医学院数据协调中心 (DCC UMMS）为 AlcHepNet 提供统计和数据管理领导力：（1）转化 研究，（2）基础/临床前研究，以及（3）试点临床试验，同时使用基于先前的创新 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，位于定量健康系的定量方法核心 (QMC) 科学，在 NIH 和 FDA 临床试验方面经验丰富，并实施了许多经过验证的、 通过增加技术的使用和在短时间内以最少的预算验证方法 监控工具。该提案的具体目标是： (1) 高效且有效的项目管理 - 开发强大的基础设施，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层领导 对 DCC UMMS 支持的研究的科学方法进行管理； (3) 高质量的研究 实施——通过严格的质量保证计划确保研究结果的高质量 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含一系列复杂的问题，这些问题决定了效率和质量 执行。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 框架内测量和记录方法，以便每个 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。