Data Management and Analysis Core (DMAC) for the Air pollution disrupts Inflammasome Regulation in HEart And Lung Total Health (AIRHEALTH) Study

摘要：数据管理和分析核心(DMAC) 数据管理和分析核心(DMAC)和为科学研究提供支持的总体目标 人类生物标记物暴露监测核心(HEMC)和项目1-3，通过建立和维护 保护集中式系统，以存储、共享和管理数据；设计可靠的研究；以及分析临床、 三个PPG项目的机械性和高通量数据。DMAC将与核心和项目合作 为了确保从临床和机械研究数据产生的分析以完整和严谨的方式执行， 使用兼容的定义和指标实现平稳集成，以提供可解释的结果和进展 该计划的临床和机制研究。我们的目标如下。目标1.提供最先进的 由计划中的项目生成的高质量数据的安全、集成和交互数据库 使研究人员能够广泛接触。DMAC将在研究电子数据捕获中建立一个系统 (RedCap)存储研究数据，这些数据扩展了帕克中心最先进的生物样本管理 软件平台，提供PPG平台，集成项目1-3和HEMC的数据。红帽将会 将为该计划项目收集的生物样品数据与受保护的关联患者元数据集成在一起 用于人体组织的研究。DMAC将对所有PPG数据和分析进行质量控制，以确保数据 元素是干净的。目标2.在项目中的各个项目中设计科学合理和可靠的研究。这个 DMAC由在设计临床试验、纵向研究和研究方面具有专业知识的人员组成 包括机械式终端。核心将使用基于团队科学的方法来利用这些专业知识，并 提炼问题和假设是可行的，以制定科学合理的分析计划使用最 现代统计工具，有助于解释和有意义的发现，并证明数字 通过考虑期间可用的资源，提供解决科学问题所需的样本和参与者 指定的研究时间表。目的3.对临床、机械和高通量数据进行分析。这个 DMAC将通过嵌入核心成员与PPG调查人员发展长期合作关系 进入调查人员的研究团队，领导研究的数据科学部分。使用一支成熟的团队 以科学为基础的方法，核心成员将制定和实施统计分析计划，以解决项目 旨在确保调查结果是可重现的。DMAC将对PPG调查人员进行生物统计学和 与他们的研究最相关的生物信息学，以促进在生产 跨学科研究出版物。与HEMC一起，DMAC提供了一些最具创新性的 用于对大数据集进行生物信息学和统计分析的工具，以做出最广泛和最具变革性的 空气污染相关机制研究对理解临床表型相关性的影响 病理学。

ABSTRACT: DATA MANAGEMENT AND ANALYTICS CORE (DMAC) Data Management and Analytics Core (DMAC) and the overall goals that provide support to the Scientific Human Biomarker Exposure Monitoring Core (HEMC) and Projects 1-3 by establishing and maintaining a secure centralized system to house, share, and manage data; designing robust studies; and analyzing clinical, mechanistic, and high-throughput data for the three PPG projects. DMAC will work with the cores and projects to ensure that analyses resulting from clinical and mechanistic study data are performed with integrity and rigor, using compatible definitions and metrics for smooth integration to provide interpretable findings and advance clinical and mechanistic research for the program. Our aims are as follows. Aim 1. Provide a state-of-the-art secure, integrated and interactive database of high-quality data generated by projects in the program that enables wide access to study investigators. The DMAC will build a system in Research Electronic Data Capture (REDCap) to house study data that extends the Parker Center's state-of-the-art biospecimen management software platform to provide a PPG platform that integrates data from Projects 1-3 and the HEMC. REDCap will integrate data from biospecimens collected for this program project with secured, associated patient metadata for research on human tissues. DMAC will conduct quality control for all PPG data and analyses to ensure data elements are clean. Aim 2. Design scientifically sound and robust studies across projects in the program. The DMAC consists of personnel with expertise in designing clinical trials, longitudinal studies, and studies that include mechanistic endpoints. The core will leverage such expertise using a team science-based approach and refining questions and hypotheses that are feasible to develop scientifically sound analysis plans using the most modern statistical tools that lend themselves to interpretable and meaningful findings, and to justify the number of samples and participants necessary to address scientific questions by considering resources available during the specified study timeline. Aim 3. Conduct analyses of clinical, mechanistic, and high throughput data. The DMAC will develop long-term collaborative relationships with PPG investigators by embedding core members into investigators' research teams to lead the data science component of studies. Using a well-established team science-based approach, core members will develop and implement statistical analysis plans to address project aims to assure findings are reproducible. The DMAC will educate PPG investigators in biostatistics and bioinformatics most pertinent to their research to promote effective collaboration in the production of interdisciplinary research publications. Together with the HEMC, the DMAC offers some of the most innovative tools for bioinformatic and statistical analysis of large data sets to make the broadest and most transformative impact in understanding clinical phenotype associations with mechanistic studies in air-pollution-associated pathology.