Data Coordinating Center for the NICHD Neonatal Research Network (U24)

项目摘要 新生儿医学证据基础中的关键和持续差距需要创新和严格的 多中心研究来解决这些问题。NICHD新生儿研究网络（NRN）调查了 以及新生儿治疗和管理策略的有效性。有一个至关重要的需要， 独立的数据协调中心（DCC），可提供高质量和公正的生物统计专业知识 通过以下方式帮助NRN瞄准其资源并优化其科学影响：（a）帮助NRN确定研究 高优先级主题，（B）为设计和分析严格且 可行性研究（特别是盲法随机试验及其中期监测），（c）开发有效的 有效利用有限资源的流程和系统，（d）确保研究设计的标准化， 开发、数据收集和分析，以及（e）传播研究结果。RTI一直是DCC， NRN自1998年以来，并建议在未来5年继续发挥这一作用。我们将最大限度地提高NRN科学 通过（1）通过创新的试验设计和统计学方法提高网络的科学严谨性， 解决新生儿研究中独特挑战的方法;（2）通过提供 灵活高效的数据和研究管理，促进数据共享、整合和协调; (3)通过向数据安全和监查部门报告，保护参与者安全和研究完整性 委员会;（4）提供及时的数据分析，与所有NRN研究的研究人员合作， （5）利用NRN基础设施和数据建立外部伙伴关系， 网络科学目标;（6）提供必要的后勤，通信和监管支持， 运行一个高效和富有成效的多中心临床研究网络。建议的DCC PI Abhik Das博士 在新生儿临床研究方面拥有丰富的经验，在过去的12年中领导了NRN DCC。他将 由候补PI玛丽·巴茨和卡拉班恩博士以及具有丰富NRN经验的工作人员协助。知情 根据我们过去的成就，我们将支持NRN在推进循证医学方面继续取得成功 新生儿护理该应用程序具有独特的优势，有助于推进NRN的研究议程：（1）高度 合格和积极的PI和工作人员在新生儿研究方面具有深入的知识和经验（包括 现有的NRN数据和正在进行的/计划中的研究），使NRN研究能够无缝地继续下去， 额外的培训或资源;（2）经证明的科学生产力，以质量和数量记录 RTI支持的NRN研究和出版物改变了临床实践;（3）最先进的 （4）工具、流程和系统的基础设施，这些基础设施是灵活的，并根据NRN的需求定制; 能够与NRN研究人员合作实现研究目标的多学科专家;（5）可靠的数据 为NRN定制的捕获和管理系统，以确保数据质量;以及（6）管理 提供最大的人员配置灵活性的结构，以快速响应不断变化的NRN需求。

Project Summary Critical and persistent gaps in the evidence base for neonatal medicine require innovative and rigorous multicenter studies to address them. The NICHD Neonatal Research Network (NRN) investigates the safety and efficacy of treatment and management strategies for newborn infants. There is a crucial need for an independent Data Coordinating Center (DCC) that can provide high-quality and impartial biostatistical expertise to help the NRN target its resources and optimize its scientific impact by (a) helping the NRN identify research topics of high priority, (b) providing objective statistical expertise for the design and analyses of rigorous and feasible studies (especially masked randomized trials and interim monitoring thereof), (c) developing efficient processes and systems for effective use of limited resources, (d) ensuring standardization of study design, development, data collection, and analyses, and (e) dissemination of study results. RTI has been the DCC for the NRN since 1998 and proposes to continue in this role for the next 5 years. We will maximize NRN scientific productivity by (1) enhancing the scientific rigor of the Network with innovative trial designs and statistical methods that address the unique challenges in neonatal research; (2) optimizing productivity by providing flexible and efficient data and study management, and promoting data sharing, integration and harmonization; (3) protecting participant safety and study integrity through reports to the Data Safety and Monitoring Committee; (4) providing timely data analysis, collaborating with investigators on all NRN studies and publications; (5) leveraging the NRN infrastructure and data for external partnerships that support and amplify Network scientific goals; and (6) providing the logistical, communications, and regulatory support necessary to run an efficient and productive multicenter clinical research network. Dr. Abhik Das, the proposed DCC PI, has extensive experience in neonatal clinical studies, leading the NRN DCC for the past 12 years. He will be assisted by Alternate PIs Drs. Marie Gantz and Carla Bann and staff with significant NRN experience. Informed by our past accomplishments, we will support continued success of the NRN in advancing evidence-based neonatal care. This application has unique strengths to help advance the NRN research agenda: (1) a highly qualified and motivated PI and staff with in-depth knowledge and experience in neonatal research (including existing NRN data and active/planned studies), enabling seamless continuation of NRN research without additional training or resources; (2) proven scientific productivity, documented by the quality and quantity of NRN studies and publications supported by RTI that have changed clinical practice; (3) a state-of-the-art infrastructure of tools, processes, and systems that are flexible and customized to the needs of the NRN; (4) multidisciplinary experts who can collaborate with NRN Investigators to realize research goals; (5) robust data capture and management systems customized for the NRN that ensure data quality; and (6) an administrative structure providing maximum staffing flexibility to respond quickly to changing NRN needs.