AlcHepNet DCC UMMS

AlcHepNet DCC UMMS

• 批准号: 10201424
• 负责人: Bruce A Barton
• 金额: $ 83.88万
• 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/10201424
• 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）是与肝有关的发病率和死亡率的主要原因，显着匮乏 有效的治疗学。 NIAAA赞助了许多RFA，以形成酒精性肝炎网络 （Alchepnet）。该网络将协同努力和专业知识，以更好地理解AH并发展 新颖的有效和安全的疗法，用于严重AH。因为将提出的研究多样 在这些RFA下，将建立两个数据协调中心，以更好地支持这些研究 并有效地从每项资助的研究中产生高质量的可重复研究。 该申请是针对马萨诸塞大学医学院（DCC）的数据协调中心 UMMS）对于Alchepnet，它将为：（1）翻译提供统计和数据管理领导 研究，（2）基础/临床前研究，以及（3）使用基于以前的创新的试验临床试验 以较短的时间表和更高质量来促进这些研究的运作的经验。 DCC UMMS团队，位于定量健康部的定量方法核心（QMC）中 科学在NIH和FDA临床试验中都经验丰富，并实施了许多经过验证的， 经过验证的方法在短时间内，预算最少，随着技术的使用加剧和 监视工具。该提案的具体目的是：（1）高效有效的项目管理 - 建立强大的基础架构，包括沟通支持和行政服务，以促进学习 目标； （2）严格的设计和分析 - 在生物统计学和数据方面提供高级领导 管理DCC UMMS支持研究的科学方法论； （3）高质量研究 实施 - 通过严格的质量保证计划确保高质量的研究结果和 控制，研究监测以及DSMB和监管报告。 这些特定目的中的每一个都包括一组复杂的问题，这些问题决定了效率和质量 执行。每个方法都将使用多方面的方法来解决，该方法可以解决多个问题 一次。虽然需要对整个研究不断变化的需求做出敏捷的响应，但也需要 在DCC UMMS的程序/SOP框架内测量和记录的方法，以便每个方法 方法集成到DCC UMMS系统中，并有助于更高质量，更可靠的数据，并且 可再现的研究。